This document presents a new approach for content-based image retrieval that combines color, texture, and a binary tree structure to describe images and their features. Color histograms in HSV color space and wavelet texture features are extracted as low-level features. A binary tree partitions each image into regions based on color and represents higher-level spatial relationships. The performance of the proposed system is evaluated on a subset of the COREL image database and compared to the SIMPLIcity image retrieval system. Experimental results show the proposed system has better retrieval performance than SIMPLIcity in some categories and comparable performance in others.
This document summarizes an evaluation of texture feature extraction methods for content-based image retrieval, including co-occurrence matrices, Tamura features, and Gabor filters. The evaluation tested these methods on a Corel image collection using Manhattan distance as the similarity measure. Co-occurrence matrices performed best with homogeneity as the feature, while Gabor wavelets showed better performance for homogeneous textures of fixed sizes. Tamura features performed poorly with directionality. Overall, co-occurrence matrices provided the best results for general texture retrieval.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Content based image retrieval based on shape with texture featuresAlexander Decker
This document describes a content-based image retrieval system that extracts shape and texture features from images. It uses the HSV color space and wavelet transform for feature extraction. Color features are extracted by quantizing the H, S, and V components of HSV into unequal intervals based on human color perception. Texture features are extracted using wavelet transforms. The color and texture features are then combined to form a feature vector for each image. During retrieval, the similarity between a query image and images in the database is measured using the Euclidean distance between their feature vectors. The results show that retrieving images using HSV color features provides more accurate results and faster retrieval times compared to using RGB color features.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Feature integration for image information retrieval using image mining techni...iaemedu
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
This document presents research on content-based image retrieval using color and texture features. It proposes using both quadratic distance based on color histograms to measure color similarity, and pyramid structure wavelet transforms and gray level co-occurrence matrix (GLCM) to measure texture. For color features, quadratic distance is calculated between color histograms to retrieve similar images based on color. For texture, pyramid structure wavelet transforms are used to decompose images into sub-bands and calculate energy levels, while GLCM extracts texture statistics. The methods are evaluated on a dataset of 10000 images and results show the integrated approach of color and texture features provides more accurate and faster retrieval compared to individual features.
This document summarizes an evaluation of texture feature extraction methods for content-based image retrieval, including co-occurrence matrices, Tamura features, and Gabor filters. The evaluation tested these methods on a Corel image collection using Manhattan distance as the similarity measure. Co-occurrence matrices performed best with homogeneity as the feature, while Gabor wavelets showed better performance for homogeneous textures of fixed sizes. Tamura features performed poorly with directionality. Overall, co-occurrence matrices provided the best results for general texture retrieval.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Content based image retrieval based on shape with texture featuresAlexander Decker
This document describes a content-based image retrieval system that extracts shape and texture features from images. It uses the HSV color space and wavelet transform for feature extraction. Color features are extracted by quantizing the H, S, and V components of HSV into unequal intervals based on human color perception. Texture features are extracted using wavelet transforms. The color and texture features are then combined to form a feature vector for each image. During retrieval, the similarity between a query image and images in the database is measured using the Euclidean distance between their feature vectors. The results show that retrieving images using HSV color features provides more accurate results and faster retrieval times compared to using RGB color features.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Feature integration for image information retrieval using image mining techni...iaemedu
This document discusses feature extraction techniques for image information retrieval. It proposes integrating features using image mining to generate a super set of features. It describes extracting primitive features of color, texture, and shape. Color is extracted using histograms in RGB color space. Texture is extracted statistically using co-occurrence matrices and wavelet transforms. Shape is extracted using boundary-based and region-based methods like Canny edge detection. The document asserts that integrating features, such as color and texture or texture and shape, results in better performance than using features individually for image retrieval.
This document presents research on content-based image retrieval using color and texture features. It proposes using both quadratic distance based on color histograms to measure color similarity, and pyramid structure wavelet transforms and gray level co-occurrence matrix (GLCM) to measure texture. For color features, quadratic distance is calculated between color histograms to retrieve similar images based on color. For texture, pyramid structure wavelet transforms are used to decompose images into sub-bands and calculate energy levels, while GLCM extracts texture statistics. The methods are evaluated on a dataset of 10000 images and results show the integrated approach of color and texture features provides more accurate and faster retrieval compared to individual features.
This document summarizes image indexing and its features. It discusses that image indexing is used to retrieve similar images from a database based on extracted features like color, shape, and texture. Color features can be represented by models like RGB, HSV, and color histograms. Shape features include global properties like roundness and local features like edge segments. Texture is described using statistical, structural, and spectral approaches. Texture feature extraction methods discussed include standard wavelets, Gabor wavelets, and extracting features like entropy and standard deviation. The paper provides an overview of the different features used for image indexing and classification.
Texture based feature extraction and object trackingPriyanka Goswami
This document provides a project report on texture-based feature extraction and object tracking. It discusses using various texture analysis techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), and Local Ternary Pattern (LTP) to extract features from images for tasks like cloud tracking. It implements these techniques in MATLAB and evaluates them on standard datasets to extract features and represent images with histograms for tasks like image recognition and analysis while reducing computational requirements compared to using raw images. The techniques are then applied to track cloud motion in weather satellite images by analyzing differences in texture histograms over time.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
This document provides a review of various texture classification approaches and texture datasets. It begins with an introduction to texture classification and its general framework. Key steps in texture classification are preprocessing, feature extraction, and classification. The document then discusses several common feature extraction methods used in texture classification, including local binary pattern (LBP), scale invariant feature transform (SIFT), speeded up robust features (SURF), Fourier transformation, texture spectrum, and gray level co-occurrence matrix (GLCM). It also reviews three popular classifiers for texture classification: K-nearest neighbors (K-NN), artificial neural network (ANN), and support vector machine (SVM). Finally, it mentions several popular texture datasets that are commonly used for training and testing texture
Amalgamation of contour, texture, color, edge, and spatial features for effic...eSAT Journals
Abstract From the past few years, Content based image retrieval (CBIR) has been a progressive and curious research area. Image retrieval is a process of extraction of the set of images from the available image database resembling the query image. Many CBIR techniques have been proposed for relevant image recoveries. However most of them are based on a particular feature extraction like texture based recovery, color based retrieval system etc. Here in this paper we put forward a novel technique for image recovery based on the integration of contour, texture, color, edge, and spatial features. Contourlet decomposition is employed for the extraction of contour features such as energy and standard deviation. Directionality and anisotropy are the properties of contourlet transformation that makes it an efficient technique. After feature extraction of query and database images, similarity measurement techniques such as Squared Euclidian and Manhattan distance were used to obtain the top N image matches. The simulation results in Matlab show that the proposed technique offers a better image retrieval. Satisfactory precision-recall rate is also maintained in this method. Keywords: Contourlet Decomposition, Local Binary Pattern, Squared Euclidian Distance, Manhattan Distance
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automatic dominant region segmentation for natural imagescsandit
Image Segmentation segments an image into different homogenous regions. An efficient
semantic based image retrieval system divides the image into different regions separated by
color or texture sometimes even both. Features are extracted from the segmented regions and
are annotated automatically. Relevant images are retrieved from the database based on the
keywords of the segmented region In this paper, automatic image segmentation is proposed to
obtained dominant region of the input natural images. Dominant region are segmented and
results are obtained . Results are also recorded in comparison to JSEG algorithm
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
This document presents a hybrid approach for color image segmentation that integrates color edge information and seeded region growing. It uses color edge detection in CIE L*a*b color space to select initial seed regions and guide region growth. Seeded region growing is performed based on color similarity between pixels. The edge map and region map are fused to produce homogeneous regions with closed boundaries. Small regions are then merged. The approach is tested on images from the Berkeley segmentation dataset and produces reasonably good segmentation results by combining color and edge information.
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
Wavelet-Based Color Histogram on Content-Based Image RetrievalTELKOMNIKA JOURNAL
The growth of image databases in many domains, including fashion, biometric, graphic design,
architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used
for finding relevant images from those huge and unannotated image databases based on low-level features
of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH)
on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database
containing 1000 color images. The experiment results show that 2nd level WBCH gives better precision
(0.777) than the other methods, including 1st level WBCH, Color Histogram, Color Co-occurrence Matrix,
and Wavelet texture feature. It can be concluded that the 2nd Level of WBCH can be applied to CBIR system.
Content Based Image Retrieval Approach Based on Top-Hat Transform And Modifie...cscpconf
In this paper a robust approach is proposed for content based image retrieval (CBIR) using texture analysis techniques. The proposed approach includes three main steps. In the first one, shape detection is done based on Top-Hat transform to detect and crop object part of the image. Second step is included a texture feature representation algorithm using color local binary patterns (CLBP) and local variance features. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Low noise sensitivity, rotation invariant, shift invariant, gray scale invariant and low computational complexity are some of other advantages.
The document describes an algorithm for detecting text in camera-captured images. It begins with preprocessing steps like converting the color image to grayscale, applying edge detection and morphological operations like dilation and erosion. This gives initial bounding boxes containing candidate text regions. Further processing includes applying geometrical constraints to filter boxes, performing multiresolution analysis, connected component analysis and filtering by area to get the final text regions. Inversion and addition steps are used to handle text against different backgrounds.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
This document proposes a new method for segmenting outdoor images called Color Cluster Elimination (CCE) which utilizes color clustering and texture analysis. CCE performs color clustering in a multi-resolution pyramid to gradually eliminate larger color clusters, preventing them from dominating segmentation and allowing smaller clusters to emerge more clearly. It then examines regions for adjacent homochromatic objects with different textures, introducing Texture Sewn Response (TSR) to indicate texture strength across resolutions/directions. The method is evaluated on the BSDS500 dataset against other metrics, demonstrating satisfactory performance for outdoor scene segmentation.
Multi Resolution features of Content Based Image RetrievalIDES Editor
Many content based retrieval systems have been
proposed to manage and retrieve images on the basis of their
content. In this paper we proposed Color Histogram, Discrete
Wavelet Transform and Complex Wavelet Transform
techniques for efficient image retrieval from huge database.
Color Histogram technique is based on exact matching of
histogram of query image and database. Discrete Wavelet
transform technique retrieves images based on computation
of wavelet coefficients of subbands. Complex Wavelet
Transform technique includes computation of real and
imaginary part to extract the details from texture. The
proposed method is tested on COREL1000 database and
retrieval results have demonstrated a significant improvement
in precision and recall.
Image segmentation involves grouping similar image components, such as pixels, into segments. It has applications in medical imaging, satellite imagery, and video summarization. Common methods include thresholding, k-means clustering, and region-based approaches. Thresholding segments an image based on pixel intensity values, while k-means clustering groups pixels into a specified number of clusters based on color or other feature similarity. Region-based methods grow or merge regions of similar pixels. Watershed segmentation treats an image as a topographic surface and finds boundaries between regions.
Ijaems apr-2016-16 Active Learning Method for Interactive Image RetrievalINFOGAIN PUBLICATION
With many possible multimedia applications, content-based image retrieval (CBIR) has recently gained more interest for image management and web search. CBIR is a technique that utilizes the visual content of an image, to search for similar images in large-scale image databases, according to a user’s concern. In image retrieval algorithms, retrieval is according to feature similarities with respect to the query, ignoring the similarities among images in database. To use the feature similarities information, this paper presents the k-means clustering algorithm to image retrieval system. This clustering algorithm optimizes the relevance results by firstly clustering the similar images in the database. In this paper, we are also implementing wavelet transform which demonstrates significant rough and precise filtering. We also apply the Euclidean distance metric and input a query image based on similarity features of which we can retrieve the output images. The results show that the proposed approach can greatly improve the efficiency and performances of image retrieval.
. Color and texture-based image segmentation using the expectation-maximizat...irisshicat
This document proposes a new image representation called "blobworld" for content-based image retrieval. It uses EM segmentation on combined color and texture features to segment images into coherent "blobs". The system allows users to view an image's internal blobworld representation to better understand query results. It aims to improve on existing systems by recognizing images as combinations of objects rather than just "stuff".
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document summarizes image indexing and its features. It discusses that image indexing is used to retrieve similar images from a database based on extracted features like color, shape, and texture. Color features can be represented by models like RGB, HSV, and color histograms. Shape features include global properties like roundness and local features like edge segments. Texture is described using statistical, structural, and spectral approaches. Texture feature extraction methods discussed include standard wavelets, Gabor wavelets, and extracting features like entropy and standard deviation. The paper provides an overview of the different features used for image indexing and classification.
Texture based feature extraction and object trackingPriyanka Goswami
This document provides a project report on texture-based feature extraction and object tracking. It discusses using various texture analysis techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), and Local Ternary Pattern (LTP) to extract features from images for tasks like cloud tracking. It implements these techniques in MATLAB and evaluates them on standard datasets to extract features and represent images with histograms for tasks like image recognition and analysis while reducing computational requirements compared to using raw images. The techniques are then applied to track cloud motion in weather satellite images by analyzing differences in texture histograms over time.
An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
This document provides a review of various texture classification approaches and texture datasets. It begins with an introduction to texture classification and its general framework. Key steps in texture classification are preprocessing, feature extraction, and classification. The document then discusses several common feature extraction methods used in texture classification, including local binary pattern (LBP), scale invariant feature transform (SIFT), speeded up robust features (SURF), Fourier transformation, texture spectrum, and gray level co-occurrence matrix (GLCM). It also reviews three popular classifiers for texture classification: K-nearest neighbors (K-NN), artificial neural network (ANN), and support vector machine (SVM). Finally, it mentions several popular texture datasets that are commonly used for training and testing texture
Amalgamation of contour, texture, color, edge, and spatial features for effic...eSAT Journals
Abstract From the past few years, Content based image retrieval (CBIR) has been a progressive and curious research area. Image retrieval is a process of extraction of the set of images from the available image database resembling the query image. Many CBIR techniques have been proposed for relevant image recoveries. However most of them are based on a particular feature extraction like texture based recovery, color based retrieval system etc. Here in this paper we put forward a novel technique for image recovery based on the integration of contour, texture, color, edge, and spatial features. Contourlet decomposition is employed for the extraction of contour features such as energy and standard deviation. Directionality and anisotropy are the properties of contourlet transformation that makes it an efficient technique. After feature extraction of query and database images, similarity measurement techniques such as Squared Euclidian and Manhattan distance were used to obtain the top N image matches. The simulation results in Matlab show that the proposed technique offers a better image retrieval. Satisfactory precision-recall rate is also maintained in this method. Keywords: Contourlet Decomposition, Local Binary Pattern, Squared Euclidian Distance, Manhattan Distance
The development of multimedia system technology in Content based Image Retrieval (CBIR) System is
one in every of the outstanding area to retrieve the images from an oversized collection of database. The feature
vectors of the query image are compared with feature vectors of the database images to get matching images.It is
much observed that anyone algorithm isn't beneficial in extracting all differing kinds of natural images. Thus an
intensive analysis of certain color, texture and shape extraction techniques are allotted to spot an efficient CBIR
technique that suits for a selected sort of images. The Extraction of an image includes feature description and
feature extraction. During this paper, we tend to projected Color Layout Descriptor (CLD), grey Level Co-
Occurrences Matrix (GLCM), Marker-Controlled Watershed Segmentation feature extraction technique that
extract the matching image based on the similarity of Color, Texture and shape within the database. For
performance analysis, the image retrieval timing results of the projected technique is calculated and compared
with every of the individual feature.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Automatic dominant region segmentation for natural imagescsandit
Image Segmentation segments an image into different homogenous regions. An efficient
semantic based image retrieval system divides the image into different regions separated by
color or texture sometimes even both. Features are extracted from the segmented regions and
are annotated automatically. Relevant images are retrieved from the database based on the
keywords of the segmented region In this paper, automatic image segmentation is proposed to
obtained dominant region of the input natural images. Dominant region are segmented and
results are obtained . Results are also recorded in comparison to JSEG algorithm
A Review of Feature Extraction Techniques for CBIR based on SVMIJEEE
As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.
This document presents a hybrid approach for color image segmentation that integrates color edge information and seeded region growing. It uses color edge detection in CIE L*a*b color space to select initial seed regions and guide region growth. Seeded region growing is performed based on color similarity between pixels. The edge map and region map are fused to produce homogeneous regions with closed boundaries. Small regions are then merged. The approach is tested on images from the Berkeley segmentation dataset and produces reasonably good segmentation results by combining color and edge information.
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
Wavelet-Based Color Histogram on Content-Based Image RetrievalTELKOMNIKA JOURNAL
The growth of image databases in many domains, including fashion, biometric, graphic design,
architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used
for finding relevant images from those huge and unannotated image databases based on low-level features
of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH)
on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database
containing 1000 color images. The experiment results show that 2nd level WBCH gives better precision
(0.777) than the other methods, including 1st level WBCH, Color Histogram, Color Co-occurrence Matrix,
and Wavelet texture feature. It can be concluded that the 2nd Level of WBCH can be applied to CBIR system.
Content Based Image Retrieval Approach Based on Top-Hat Transform And Modifie...cscpconf
In this paper a robust approach is proposed for content based image retrieval (CBIR) using texture analysis techniques. The proposed approach includes three main steps. In the first one, shape detection is done based on Top-Hat transform to detect and crop object part of the image. Second step is included a texture feature representation algorithm using color local binary patterns (CLBP) and local variance features. Finally, to retrieve mostly closing matching images to the query, log likelihood ratio is used. The performance of the proposed approach is evaluated using Corel and Simplicity image sets and it compared by some of other well-known approaches in terms of precision and recall which shows the superiority of the proposed approach. Low noise sensitivity, rotation invariant, shift invariant, gray scale invariant and low computational complexity are some of other advantages.
The document describes an algorithm for detecting text in camera-captured images. It begins with preprocessing steps like converting the color image to grayscale, applying edge detection and morphological operations like dilation and erosion. This gives initial bounding boxes containing candidate text regions. Further processing includes applying geometrical constraints to filter boxes, performing multiresolution analysis, connected component analysis and filtering by area to get the final text regions. Inversion and addition steps are used to handle text against different backgrounds.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
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Multi Resolution features of Content Based Image RetrievalIDES Editor
Many content based retrieval systems have been
proposed to manage and retrieve images on the basis of their
content. In this paper we proposed Color Histogram, Discrete
Wavelet Transform and Complex Wavelet Transform
techniques for efficient image retrieval from huge database.
Color Histogram technique is based on exact matching of
histogram of query image and database. Discrete Wavelet
transform technique retrieves images based on computation
of wavelet coefficients of subbands. Complex Wavelet
Transform technique includes computation of real and
imaginary part to extract the details from texture. The
proposed method is tested on COREL1000 database and
retrieval results have demonstrated a significant improvement
in precision and recall.
Image segmentation involves grouping similar image components, such as pixels, into segments. It has applications in medical imaging, satellite imagery, and video summarization. Common methods include thresholding, k-means clustering, and region-based approaches. Thresholding segments an image based on pixel intensity values, while k-means clustering groups pixels into a specified number of clusters based on color or other feature similarity. Region-based methods grow or merge regions of similar pixels. Watershed segmentation treats an image as a topographic surface and finds boundaries between regions.
Ijaems apr-2016-16 Active Learning Method for Interactive Image RetrievalINFOGAIN PUBLICATION
With many possible multimedia applications, content-based image retrieval (CBIR) has recently gained more interest for image management and web search. CBIR is a technique that utilizes the visual content of an image, to search for similar images in large-scale image databases, according to a user’s concern. In image retrieval algorithms, retrieval is according to feature similarities with respect to the query, ignoring the similarities among images in database. To use the feature similarities information, this paper presents the k-means clustering algorithm to image retrieval system. This clustering algorithm optimizes the relevance results by firstly clustering the similar images in the database. In this paper, we are also implementing wavelet transform which demonstrates significant rough and precise filtering. We also apply the Euclidean distance metric and input a query image based on similarity features of which we can retrieve the output images. The results show that the proposed approach can greatly improve the efficiency and performances of image retrieval.
. Color and texture-based image segmentation using the expectation-maximizat...irisshicat
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Color and texture based image retrievaleSAT Journals
Abstract Content-based image retrieval (CBIR) is an vital research area for manipulating bulky image databases and records. Alongside the conventional method where the images are searched on the basis of words, CBIR system uses visual contents to retrieve the images. In content based image retrieval systems texture and color features have been the primal descriptors. We use HSV color information and mean of the image as texture information. The performance of proposed scheme is calculated on the basis of precision, recall and accuracy. As an effect, the blend of color and texture features of the image provides strong feature set for image retrieval. Keywords: image retrieval, HSV color space, color histogram, image texture.
Invention of digital technology has lead to increase in the number of images that can be stored in digital format. So searching and retrieving images in large image databases has become more challenging. From the last few years, Content Based Image Retrieval (CBIR) gained increasing attention from researcher. CBIR is a system which uses visual features of image to search user required image from large image
database and user’s requests in the form of a query image. Important features of images are colour, texture and shape which give detailed information about the image. CBIR techniques using different feature extraction techniques are discussed in this paper.
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
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Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
Content Based Image Retrieval Using Dominant Color and Texture FeaturesIJMTST Journal
The purpose of this Paper is to describe our research on different feature extraction and matching techniques in designing a Content Based Image Retrieval (CBIR) system. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. Content Based Image Retrieval (CBIR) is the retrieval of images based on features such as color and texture. Image retrieval using color feature cannot provide good solution for accuracy and efficiency. The most important features are Color and texture. In this paper technique used for retrieving the images based on their content namely dominant color, texture and combination of both color and texture. The technique verifies the superiority of image retrieval using multi feature than the single feature.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
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A New Method for Indoor-outdoor Image Classification Using Color Correlated T...CSCJournals
In this paper a new method for indoor-outdoor image classification is presented; where the concept of Color Correlated Temperature is used to extract distinguishing features between the two classes. In this process, using Hue color component, each image is segmented into different color channels and color correlated temperature is calculated for each channel. These values are then incorporated to build the image feature vector. Besides color temperature values, the feature vector also holds information about the color formation of the image. In the classification phase, KNN classifier is used to classify images as indoor or outdoor. Two different datasets are used for test purposes; a collection of images gathered from the internet and a second dataset built by frame extraction from different video sequences from one video capturing device. High classification rate, compared to other state of the art methods shows the ability of the proposed method for indoor-outdoor image classification.
Query Image Searching With Integrated Textual and Visual Relevance Feedback f...IJERA Editor
There are many researchers who have studied the relevance feedback in the literature of content based image
retrieval (CBIR) community, but none of CBIR search engines support it because of scalability, effectiveness
and efficiency issues. In this, we had implemented an integrated relevance feedback for retrieving of web
images. Here, we had concentrated on integration of both textual features (TF) and visual features (VF) based
relevance feedback (RF), simultaneously we also tested them individually. The TFRF employs and effective
search result clustering (SRC) algorithm to get salient phrases. Then a new user interface (UI) is proposed to
support RF. Experimental results show that the proposed algorithm is scalable, effective and accurated
Performance analysis is basis on color based image retrieval techniqueIAEME Publication
This document discusses performance analysis of color-based image retrieval techniques. It proposes using foreground color extraction and K-nearest neighbor classification to retrieve similar images based on foreground objects. The key steps are segmenting images to separate foreground from background, categorizing foreground colors, and matching query images to images in a database based on dominant foreground color using K-nearest neighbor. An experimental analysis on a celebrity image dataset found the proposed technique achieved higher precision and recall than existing background-focused methods.
Performance analysis is basis on color based image retrieval techniqueIAEME Publication
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This document summarizes a research paper on perceptual color image segmentation using k-means clustering. It begins with an introduction to image segmentation and discusses how clustering can be used. It then reviews different color models (e.g. RGB, CIELAB, HSV) and their suitability for segmentation. The proposed method segments images into perceptual partitions based on hue values using k-means clustering. Results on natural images demonstrate the ability to extract meaningful regions. The technique works well but has higher time complexity than other methods.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retriev...IJERA Editor
This document evaluates the performance of the Euclidean and Manhattan distance metrics in a content-based image retrieval system. It finds that the Manhattan distance metric showed better precision than the Euclidean distance metric. The system uses color histograms and Gabor texture features to represent images. Color is represented in HSV color space and histograms of hue, saturation and value are used. Gabor filters are applied to capture texture at different scales and orientations. Distance between feature vectors is calculated using Euclidean and Manhattan distance formulas to find similar images from the database. The system was tested on a dataset of 1000 Corel images and Manhattan distance produced more relevant search results.
Performance Evaluation Of Ontology And Fuzzybase Cbiracijjournal
In This Paper, We Have Done Performance Evaluation Of Ontology Using Low-Level Features Like
Color, Texture And Shape Based Cbir, With Topic Specific Cbir.The Resulting Ontology Can Be Used
To Extract The Appropriate Images From The Image Database. Retrieving Appropriate Images From An
Image Database Is One Of The Difficult Tasks In Multimedia Technology. Our Results Show That The
Values Of Recall And Precision Can Be Enhanced And This Also Shows That Semantic Gap Can Also Be
Reduced. The Proposed Algorithm Also Extracts The Texture Values From The Images Automatically
With Also Its Category (Like Smooth, Course Etc) As Well As Its Technical Interpretation
PERFORMANCE EVALUATION OF ONTOLOGY AND FUZZYBASE CBIRacijjournal
IN THIS PAPER, WE HAVE DONE PERFORMANCE EVALUATION OF ONTOLOGY USING LOW-LEVEL FEATURES LIKE
COLOR, TEXTURE AND SHAPE BASED CBIR, WITH TOPIC SPECIFIC CBIR.THE RESULTING ONTOLOGY CAN BE USED
TO EXTRACT THE APPROPRIATE IMAGES FROM THE IMAGE DATABASE. RETRIEVING APPROPRIATE IMAGES FROM AN
IMAGE DATABASE IS ONE OF THE DIFFICULT TASKS IN MULTIMEDIA TECHNOLOGY. OUR RESULTS SHOW THAT THE
VALUES OF RECALL AND PRECISION CAN BE ENHANCED AND THIS ALSO SHOWS THAT SEMANTIC GAP CAN ALSO BE
REDUCED. THE PROPOSED ALGORITHM ALSO EXTRACTS THE TEXTURE VALUES FROM THE IMAGES AUTOMATICALLY
WITH ALSO ITS CATEGORY (LIKE SMOOTH, COURSE ETC) AS WELL AS ITS TECHNICAL INTERPRETATION.
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Content-based Image Retrieval Using The knowledge of Color, Texture in Binary Tree Structure
1. Content based image retrieval using the knowledge of texture, color in
binary tree structure
Zahra Mansoori, Mansour Jamzad
Z_mansoori@ce.sharif.edu, Jamzad@sharif.edu
Sharif University of technology, Tehran, Iran
ABSTRACT
Content base image retrieval is an important research field
with many applications. This paper presents a new approach
for finding similar images to a given query in a general-
purpose image database using content-based image retrieval.
Color and Texture are used as basic features to describe
images. In addition, a binary tree structure is used to
describe higher level features of an image. It has been used
to keep information about separate segments of the images.
The performance of the proposed system has been compared
with the SIMPLIcity system using COREL image database.
our experimental results showed that among 10 image
categories available in COREL database, our system had a
better performance (10% average) in four categories, equal
performance in two and lower performance (7% average)
for the remaining four categories.
Index Terms— Content-based Image retrieval, Color,
texture, Binary Tree Partitioning
1. INTRODUCTION
In recent years, content-based image retrieval (CBIR)
has played an important role in many fields, such as
medicine, geography, weather forecasting, security, etc.
These approaches are based on visual attributes of images
such as color, texture, shape, layout and object. Most of the
content-based image retrieval systems are designed to find
the top N images that are most similar to the user query
image [1, 2].
In this paper the approach is to combine color, Texture
and a customized binary partitioning tree in order to find the
images similar to a specific query image. The above
mentioned tree is a customized binary partitioning tree
which keeps a combination of color and layout information
of an image. To extract color information, two histograms of
the image in HSV mode are used with 360 and 100 bins.
Also a 2-levels Wavelet decomposition of separated image
blocks is used to attain texture. The binary tree is used to
maintain information about separated regions of the image.
2. OVERALL STRUCTURE
The overall structure of almost all CBIR systems typically
consist of two Independent parts: Feature Extraction and
Retrieval. The first part extracts visual information from the
image and saves them in a database, where the second part
searches the maintained information based on defined
conditions to find the matching images from database. The
details of composition and functionality of these parts vary
among different systems.
The overall structure of a typical image retrieval system
is shown in Fig.1, which has seven separated parts: 1. Image
database consisting of hundreds or thousands of images
among which a query image is searched; 2. Feature
Extraction which retrieves features from images and sends
them to appropriate parts; 3. Database of extracted features
received from part 2; 4. Query image which is an image
input by the user in order to get the similar images; 5.
Feature Vectors of query image extracted by part 2; 6.
Search and retrieval part which searches the Feature Vectors
database in order to get similar images to query image; 7.
User interface which shows the retrieved images from part
6.
Fig.1. Overall structure of an image retrieval system (from ]
3
,
4
[ )
In the proposed approach, feature extraction is divided
into two levels, low level feature extraction that extracts
Color and Texture features, and the description of Binary
Tree Structure in retrieval process.
3. FEATURE EXTRACTION
Technically, any image can be considered as a 2-
Dimonsional array of pixels. Feature extraction is a way to
show visual information of an image in scale of numbers so
they can be analogous.
4. Query
Image
5. Query
Image F.V.
1. Image DB
3. F.V.s DB
6. Search and Retrieval
7. End user
2. Feature Extraction
2. 3.1. Color Extraction
Color is represented in a 3-channel color space. There are
various color spaces such as RGB, HSV, YCbCr, CIE LAB,
CIE LUV, etc. however, no color space is dominant in all
applications. In this paper, the HSV color space is used
because it is a perceptual color space. That is, the three
components H (Hue), S (Saturation) and V (Value)
correspond to the color attributes closely associated with the
way that the human eye perceives the color. Hue indicates
the type of color, such as red, green and blue, which
corresponds to the dominant wavelength of a given
perceived color stimulus. Saturation refers to the strength of
a color. A fully saturated color contains only a single
wavelength. The color becomes less saturated when white
light is added to it. Value (or intensity) is the amount of
light perceived from a given color sensation. White is
perceived to be maximum intensity and black to be the
minimum intensity
The approach here is two extract two histograms, one for
Hue and one for Saturation. Due to the fact that the Value
dimension of color in HSV is too variant by lightness degree
of photography, so it is not a valid measure to judge how
two images are similar, so it is not considered in calculation.
The Hue circle of HSV color space has been quantized into
360 degrees, and saturation into 100 levels. Thus the
corresponding histograms have 360 and 100 bins.
3.2. Texture Extraction
Texture is a key component of human visual perception.
Like color, texture is an essential feature to be considered
when querying image databases [5]. Generally speaking,
textures are complex visual patterns composed of entities, or
sub-patterns which have characteristic brightness, color,
slope, size, etc. Therefore texture can be regarded as a
similarity grouping in an image [6].
For texture extraction, Wavelet decomposition of image
blocks is used. By imposing Wavelet on a gray-level image,
four sub images will be produced, which is a low resolution
copy (Approximation) image, and three-band passed filters
in specific directions: horizontal, vertical and diagonal
respectively. These sub images contain useful information
about image texture characteristics. To have a numerical
presentation of the texture, mean and variation of these
images will be calculated.
Final feature vector will be gained by 1. Dividing image into
8×8 = 64 equal blocks; 2. Applying Wavelet on each block.
3. Calculating Mean and Variation of each block and
concatenating them, separately; 4. Concatenating all
obtained feature vectors to achieve two feature vectors
describing texture information.
3.3. Binary Partitioning Tree
A Binary Partition Tree is a structured representation of the
regions of an image. An example is shown in Fig.2. The
leaves of the tree represent regions belonging to the initial
partition (partition 1) and the remaining nodes represent
regions that are obtained by merging the regions represented
by the two children of a node. The root node represents the
entire image. This representation should be considered as a
compromise between representation accuracy and
processing efficiency. The main advantage of the tree
representation is that it allows the fast implementation of
sophisticated partitioning process [8].
Fig.2. An example of Binary Partition Tree creation with a
region merging algorithm (derived from [8])
In this paper, a simplified and efficient use of binary tree
has been proposed. There are some important considerations
in the way to create the partitioning tree to obtain better
performance.
3.3.1. Image Partitioning
There are various ways to partition an image into separate
regions, but the most important consideration is that each
partition should be meaningful, which in best case, contains
dedicated information about an object. An object may have
a homogeneous color [8], Color & texture [9] or none which
in this case, defining an object may be impossible and this
case can be eliminated. The approach of this paper is based
on color homogeneity; batches of similar colors will signify
objects/regions. This is achieved by using safe color cube as
a primary color palette. It is demonstrated that this palette
works well as it covers RGB color space as well, which after
quantization, well equivalent of row image will be gained.
Safe Color Cube consists of 216 colors in RGB mode;
each R, G and B can only be 0, 51, 102, 153, 204 or 255.
Thus, RGB triples of these values give us (6)3
= 216
possible values [10] (Fig. 3.). To represent a picture, for
each pixel or batch of pixels the equivalent color in the
palette will be found and replaced. For better precision,
mean of a batch of pixels should be used. New color will be
replaced with the old one. This process is called
quantization. By having an image with 216 specific colors,
it is expected to have distinctive regions with homogeneous
color. By converting this image to gray-scale, we have a
partitioned image. The number of possible gray-levels will
be 216.
3. 3.3.2. Tree Construction
To construct a binary tree, the algorithm starts from an
arbitrary region as the first node and chooses a neighbor
region as its sibling and these nodes will be added as
children of their parent node. This process will be repeated
until all regions have been added to the tree.
To construct the binary tree an important point is that the
trees must be comparable with each other. One way to
achieve this is to define a fixed template for trees such that
the comparison will be done node by node. The constrains
of constructing the trees is to define 1. Maximum levels of
the tree and 2. Maximum regions that the image will be
partitioned at each level of partitioning. Doing this, all
images are represented by identical trees with equal number
of levels and nodes. The remaining problem is whether two
similar images will produce the same trees? How different
the trees will be if their corresponding images had little
differences. It is possible that in each level, the region in
two similar pictures are segmented differently, so the
starting points at each level will be different and the
resulting trees may be completely or partially incomparable.
The technical solution for the problem of similar images
is to define fixed regions at the starting point, away from
previously mentioned partitioning.
If the size of these fixed-sized regions is large, the
problem still remains; if they are small, the presence of the
tree is meaningless. The trade off may be to initialize
constructing by fixed size regions like what we used for
texture extraction approach. The approach here is to divide
the image into equal-sized regions and creating a distinct
tree for each region. Fig.3 shows an iterative algorithm for
tree construction, inputs are 1. Colored image and 2.
Number of maximum growing levels of tree, and the output
is a tree, representing the image.
INITIALIZE (MaximumTreeLevels, MaximumRegions)
GrayImage = LebeledBySafeColors ( Image)
MaxTreeL = MaximumTreeLevelsDefines
MaxReg = MaximumRegions
BINARYTREE_ITERATIVE_CONST(GrayImage, 0)
BINARYTREE_ITERATIVE_CONST(GrayImage, CurrentTreeL )
If CurrentTreeL = MaxTreeL
Return
Save( BINARY TREE_GETPROPERTIES ( GrayImage ) )
GMin = MinimumGrayLevelsOf ( GrayImage )
GMax = MaximumGrayLevelsOf ( GrayImage )
GStepsizes = ( MaxG – MinG ) / MaxReg
For i = 1 to MaxReg
LowestG = ( i – 1 ) * GStepsizes + GMin
HighestG = i * GStepsizes + GMin
SubImage=FilterGrayLevels ( GrayImage, LowestG, HighestG )
BINARYTREE_ITERATIVE_CONST(SubImage,CurrentTreeL+1)
End
Fig.3. Pseudo code algorithm for binary tree construction
3.3.3. Features
Final feature extraction will be the mean color and the
surface of the regions at each node (internal or leaves).
Surface is the number of pixels in a region, if all images are
normalized then it means we have equal sizes. Due to usage
of SIMPLicity database for comparison, all images have
approximately the same size so it is safely assumed that the
whole database is normalized.
4. SEARCH AND RETRIEVAL
After extracting features, the second main responsibility of
an image retrieval system is 'Search and retrieval'. It is
assumed that feature space is a multidimensional space and
images are scattered based on the value of their feature
vectors, so more similar the feature vector, closer are images
in this space. The Search process is to get feature vectors of
an input image called 'Query' or 'Feed' [11] and retrieve the
images in the neighborhood of that in feature space. This
search strategy is called nearest-neighborhood [12]. If we
assume that two images of database are more similar than
the others, their feature vector should have a minimum
distance; so the similarity has a reversed relationship with
the distance. So by having the difference of feature vectors
of two images, the similarity of them will be known.
4.1. Distance measure
The difference of two feature vectors should be defined in a
way that it appears perfectly as it has close relationship to
the type of feature. Most of the difference formulas are a
variation of Minkowski difference. The Minkowski distance
for two vectors or histograms ~k and ~l with dimension n is
given by equation (7) [3].
𝐷 𝑘, 𝑙̅ = ( |𝑘 − 𝑙 | ) /
(7)
Color histogram is usually measured by 𝜌 = 1 [13, 14],
so this measure is used in our approach too. For texture
level, two of the remaining distances have been used. This
form is called Euclidean distance.
Binary tree is a special case due to the type of its
features. There is a Matrix of 3-dimentional colors and an
array of surfaces. For color distance, the distance between
colors of each node is calculated by using Euclidean
distance and finally the results have been aggregated. For
the surface the same distance metric as histogram has been
used.
There may be a case that a specific region assigned to an
internal node has a unique gray level such that it could not
be divided into some sub-regions and one of its children in
binary tree may have zero value for Color and Surface
property. At the time of calculating the difference, these
nodes will not be accounted and the judgment is based on its
higher levels or its sibling.
4. 4.2. Final Query Ranking
Finally, the feature distances should be summed in order to
have a final distance. The prerequisite for this operation is
normalization in each feature level, by dividing all distance
values of a specific feature to the maximum gained distance.
Adding all the difference values gets a measure of how
an image is different from the query image. By reversing
this value, the 'Rank' of each image will be calculated. The
most k high-rank images will be chosen to display to the
user.
The main issue for each feature at the retrieval level is
how efficient it is; in other words how much of the
performance of the system depends on it. To make it clear
there are coefficients assigned to each feature vector.
Final ranking will be achieved from equations (8) ~ (11).
𝑟 = 𝑟 , + 𝑟 , + 𝑟 , (8)
𝑟 , = 𝑅 . 𝑆 , (9)
𝑆 , = 1/𝐷𝑠 , (10)
𝐷𝑠 , =
𝐷 ,
𝑀𝐴𝑋 𝐷 ,
(11)
Where in 8, ri is final rank of ith image, 𝑟 , ,
𝑟 , , 𝑟 , are ranks of Color, Texture and Binary
Tree features, respectively. For instance, 𝑟 , is extended
in (9). 𝑅 is the coefficient of Color feature. 𝑆 , is
the similarity measure between the ith image and query
image in case of Color. As shown in (10), it is reversely
dependent to distance; 𝐷𝑠 , means the distance of ith
image from query image. In (11) normalization is carried on
by dividing the row difference of both feature vectors to the
maximum in their fields.
5. SYSTEMATIC EVALUATION
This system has been compared to SIMPLicity [15] which
is an image retrieval system which uses color, texture,
shape, and location. This system was evaluated based on a
subset of the COREL database, formed by 10 image
categories (shown in Table 2), each containing 100 pictures.
ID Category Name ID Category Name
1 Africa people & villages 6 Elephants
2 Beach 7 Flowers
3 Buildings 8 Horses
4 Buses 9 Mountains & glaciers
5 Dinosaurs 10 Food
Table 2. COREL categories of images tested
Within this database, it is known whether any two
images belong to the same category. In particular, a
retrieved image is considered a match if and only if it is in
the same category as the query. This assumption is
reasonable since the 10 categories were chosen so that each
depicts a distinct semantic topic. Every image in the sub-
database was tested as a query and the retrieval ranks of all
the rest images were recorded. Three statistics were
computed for each query: 1) the precision within the first
100 retrieved images, 2) the mean rank of all the matched
images, and 3) the standard deviation of the ranks of
matched images.
The recall within the first 100 retrieved images is
identical to the precision in this special case. The total
number of semantically related images for each query is
fixed to be 100.The average performance for each image
category is computed in terms of three statistics: p
(precision), r (the mean rank of matched images), and σ (the
standard deviation of the ranks of matched images).
6. EXPERIMENTAL RESULT
The result of partitioning a sample image with 216-color
palette is presented in Fig.4. After some experiments, the
number of tree levels and number of regions is set to 4 and
2, respectively. By choosing these parameters, the number
of empty regions has been reduced to minimum and each
partitioned region will represent the image suitably.
(a) (b)
Fig.4. Binary Tree Construction: (a) original image (b) gray
level of quantized image using 216-color palette
In order to obtain the system performance, the first step
is to define the importance of each feature. This has been
done in a step by step procedure. At first, color has been
tested with custom coefficients, in the second step a test on
both color and texture is done by assigning random
coefficients to texture. In the next step, the binary tree has
been added to feature lists and proper coefficients have been
assigned to it. The properties of each feature vector and their
coefficients are listed in Table 3.
i Visual Descriptor Feature Vector Importance
(Ri)
1 Color 360-bin Hue Hist 68%
2 100-bin Sat Hist 14%
3 Texture Wavelet Mean 6%
4 Wavelet Var 6%
5 Binary Partitioning
Tree
Color Mean 3%
6 Surface 3%
Table 3. Information of the features used in this approach
It should be noted that the extended formulas (8)~(11) are
written using nominal feature vectors. In fact, there are more
than three features, which are listed in Table 3.
5. The hue histogram with highest importance is used as a
filter to detect related or nonrelated images in retrieval
process. Three statistic parameters have been calculated for
each category of image database. The comparison results are
shown in Fig.5-7.
Fig.5. Comparing both systems on average precision for 10
categories
Fig.6. Comparing both systems on average rank of matched images
in 10 categories
Fig.7. Comparing both systems on average rank of matched images
in 10 categories
The approach in Simplicity is based on comparison of
shape. But in the proposed method, the comparison
approach is based on color, texture and the effect of
background (that is considered by a binary tree
representation of the entire image). Since in buses category
the effect of all above mentioned three factors are present, it
is expected that the proposed method shows a better
performance than Simplicity. Our experimental results
showed this for both busses and also the food categories.
To have a measure to understand how important binary
tree is, three different coefficients have been assigned to this
feature: 0% (means don't care), 45% (about half) and 3%.
The schematic is shown in Fig.8. In present system the
coefficient of 3% is used for binary tree.
Fig.8. Effectiveness of binary tree in retrieval process
Some query results are shown in Fig. 9.
Cat 1: 12 out of 14 corrects
Cat 2: 12 out of 14 corrects
Cat 3: 11 out of 14 corrects
Cat 4: 14 out of 14 corrects
Cat 5: 14 out of 14 corrects
0
0.2
0.4
0.6
0.8
1
Average
Precision
System Comparison Prefered System
SIMPLicity
1 2 3 4 5 6 7 8 9 10
0
50
100
150
200
250
300
Average
Rank
System Comparison Prefered System
SIMPLicity
1 2 3 4 5 6 7 8 9 10
0
50
100
150
200
250
300
١ ٢ ٣ ۴ ۵ ۶ ٧ ٨ ٩ ١٠
Average
Standard
deviation
of
Ranks
System Comparison Prefered System
SIMPLicity
1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
١ ٢ ٣ ۴ ۵ ۶ ٧ ٨ ٩ ١٠
Average
precition
BTree importance in retrieval
R = 3% R = 45% R = 0%
1 2 3 4 5 6 7 8 9 10
6. Cat 6: 9 out of 14 corrects
Cat 7: 14 out of 14 corrects
Cat 8: 14 out of 14 corrects
Cat 9: 12 out of 14 corrects
Cat 10: 14 out of 14 corrects
Fig.9. some query Examples. In each category, the upper left
image is the query image and the remaining are retrieved images.
The query image has been chosen from DB so the first image has
been accounted as the first retrieved image with highest rank.
7. CONCLUSION
The system presented here has better performance in four
categories, worse in four categories and equal in two
categories in comparison to SIMPLicity.
It can be said that binary tree is good for increasing the
performance in categories which have more similar pictures
(for example several photos of a unique scene). It is
predictable because of high level of feature extraction used
to extract this feature, also, it has better result in categories
with similar background, and it is because of blocking effect
which extracts semi-equal feature vectors from the
background of these images.
8. REFERENCES
[1] W. M. a. H. J. Zhang, Content-based Image Indexing and
Retrieval: CRC Press, 1999.
[2] e. M. Flickers. H. Sawhney, "Query by Image and Video
Content: The QBIC System," IEEE Computers, 1995.
[3] Gevers Th. and Smeulders A.W.M., "Image Search
Engines, An Overview," The International Society for Optical
Engineering (SPIE), vol. VIII, pp. 327--337, 2003.
[4] Schettini R. ; Ciocca G. and Zuffi S., "A Survey of
Methods for Color Image Indexing and Retrieval in Image
Databases."
[5] Howarth P. and Ruger S., "Evaluation of Texture
Features for Content-Based Image Retrieval," in Third
International Conference, CIVR 2004, Dublin, Ireland, 2004.
[6] K. A. Rosenfeld A., "Digital Picture Processing,"
Academic Press, vol. 1, 1982.
[7] S. S. a. P. J. Hiremath P.S., "Wavelet Based Feature for
Color Texture Classification with application to CBIR," Intl.
Journal of Computer Science and Network Security (IJCSNS), vol.
6, Sep. 2006.
[8] P. S. a. L. Garrido, "Binary Partition Tree as an Efficient
Representation for Image Processing, Segmentation, and
Information Retrieval," IEEE TRANSACTIONS ON IMAGE
PROCESSING, vol. 9, pp. 561-576, 2000.
[9] W. J. C. Ghanbari S., Rabiee H.R., Lucas S.M.,
"Wavelet domain binary partition trees for semantic object
extraction," 2007.
[10] W. R. E. Gonzalez Rafael C., Digital Image Processing:
Prentice Hall, 2002.
[11] Smith J. R. and Chang S., "Tools and Techniques for
Color Image Retrieval," in SPIE, 1996, pp. 1630-1639.
[12] Chiueh T., "Content-based image indexing," in
Proceedings of VLDB '94, Santiago, Chile, 1994, pp. 582-593.
[13] Li X. ; Chen S. ; Shyu M. and Furht B., "An Effective
Content-Based Visual Image Retrieval System," in 26th IEEE
Computer Society International Computer Software and
Applications Conference (COMPSAC), Oxford, 2002, pp. 914-
919.
[14] Stricker M. A. and Orengo M., "Similarity of Color
Images," in SPIE, 1995, pp. 381--392.
[15] J. L. J. Z. Wang, and G. Wiederhold, "SIMPLIcity:
Semantics-Sensitive Integrated Matching for Picture Libraries,"
IEEE Trans. Patt. Anal. Mach. Intell., vol. 23, pp. 947-963, 2001.