An Information Theoretic Approach to Content Based Image Retrieval.

We propose an information theoretic approach to the representation and comparison of color features in digital images to handle various problems in the area of content-based image retrieval. The interpretation of color histograms as joint probability density functions enables the use of a wide range of concepts from information theory to be considered in the extraction of color features from images and the computation of similarity between pairs of images. The entropy of an image is a measure of the randomness of the color distribution in an image. Rather than replacing color histograms as an image representation, we demonstrate that image entropy can be used to augment color histograms for more efficient image retrieval. We propose an indexing algorithm in which image entropy is used to drastically reduce the search space for color histogram computations. Our experimental tests applied to an image database with 10,000 images suggest that the image entropy-based indexing algorithm is scalable for image retrieval of large image databases. We also proposed a new similarity measure called the maximum relative entropy measure for comparing image feature vectors that represent probability density functions. This measure is an improvement of the Kullback-Leibler number in that it is non-negative and satisfies the identity and symmetry axioms. We also propose a new usability paradigm called Query By Example Sets (QBES) that allows users, particularly novice users, the ability to express queries in terms of multiple images

A histogram-based approach for object-based query-by-shape-and-color in image and video databases

Cataloged from PDF version of article.Considering the fact that querying by low-level object features is essential in image and video data, an efficient approach for querying and retrieval by shape and color is proposed. The approach employs three specialized histograms, (i.e. distance, angle, and color histograms) to store feature-based information that is extracted from objects. The objects can be extracted from images or video frames. The proposed histogram-based approach is used as a component in the query-by-feature subsystem of a video database management system. The color and shape information is handled together to enrich the querying capabilities for content-based retrieval. The evaluation of the retrieval effectiveness and the robustness of the proposed approach is presented via performance experiments. (C) 2005 Elsevier Ltd All rights reserved

SISTEM TEMU KEMBALI CITRA KAIN BERBASIS TEKSTUR DAN WARNA

Content Based Image Retrieval is a method to find images by comparing between a query image with images in database based on information of image. CBIR used to find images in database based on similarity of colors, texture and shapes. This research will using colors and texture methosd to find similarity images in database. Method that in using for colors extraction is HSV Histograms then for texture extraction is static characteristic extraction method. This research using 30 of images from 5 different type of cloth as training and query images. Result for image retrival After performing subjective test using recall method based on texture similarity percentages 76,19%, based on color percentages 100% and based on texture and color similarity percentages 100%. Result of this study is content based image retrieval based texture and color using static characteristic extraction and HSV Histograms method can to retrieve relevan of images in database that match by query image

A content-based image retrieval system for texture and color queries

Cataloged from PDF version of article.In recent years, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Two of the main components of the visual information are texture and color. In this thesis, a content-based image retrieval system is presented that computes texture and color similarity among images. The underlying technique is based on the adaptation of a statistical approach to texture analysis. An optimal set of five second-order texture statistics are extracted from the Spatial Grey Level Dependency Matrix of each image, so as to render the feature vector for each image maximally informative, and yet to obtain a low vector dimensionality for efficiency in computation. The method for color analysis is the color histograms, and the information captured within histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features thus extracted and stored within feature vectors are later compared with an intersection-based method. The system is also extended for pre-processing images to segment regions with different textural quality, rather than operating globally over the whole image. The system also includes a framework for object-based color and texture querying, which might be useful for reducing the similarity error while comparing rectangular regions as objects. It is shown through experimental results and precision-recall analysis that the content-based retrieval system is effective in terms of retrieval and scalability.Konak, Eyüp SabriM.S

A histogram-based approach for object-based query-by-shape-and-color in image and video databases

Considering the fact that querying by low-level object features is essential in image and video data, an efficient approach for querying and retrieval by shape and color is proposed. The approach employs three specialized histograms, (i.e. distance, angle, and color histograms) to store feature-based information that is extracted from objects. The objects can be extracted from images or video frames. The proposed histogram-based approach is used as a component in the query-by-feature subsystem of a video database management system. The color and shape information is handled together to enrich the querying capabilities for content-based retrieval. The evaluation of the retrieval effectiveness and the robustness of the proposed approach is presented via performance experiments. © 2005 Elsevier Ltd. All rights reserved

Texture Based Image retrieval using Human interactive Genetic Algorithm

Content-based image retrieval has been keenly calculated in numerous fields. This provides more active management and retrieval of images than the keyword-based method. So the content based image retrieval has become one of the liveliest researches in the past few years. As earlier, we were using the text-based approach where it initiate very boring and hard task for solving the purpose of image retrieval. But the CBIR is the method where there are several methodologies are available and the task of image retrieval becomes well easier. In this, there are specific effective methods for CBIR are discussed and the relative study is made. However most of the proposed methods emphasize on finding the best representation for diverse image features. Here, the user-oriented mechanism for CBIR method based on an interactivegenetic algorithm (IGA) is proposed. Color attributes likethe mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histograms of an image are too considered as the texture features

Color Histogram Features for Image Retrieval Systems

ABSTRACT: Histogram features have proved powerful in the classification of image and object detection . The CBIR most efficient and searches the color based images. Here in this method we use some improved preprocessing steps, preprocessing algorithms and the image classification is analyzed. In CBIR image classification has to be computationally very fast and efficient. In this project a new approach is introduced, which based on low level image histogram features. Color is a main powerful descriptor that often identifies object and extraction scene. The main advantage of this method is the very quick generation and comparison of the applied feature vectors. Histograms are simple to calculate in software and also lend themselves to economic hardware implementations. A popular tool for a real-time image processing histogram-based image retrieval methods in two color spaces were exhaustively compared. The testing also highlights the weaknesses and strengths of the model. KEY WORDS: color-based and content based image retrieval, K-Nearest neighbor method, histogram intersection and its similarity

Content-Based Image Retreival for Detecting Brain Tumors and Amyloid Fluid Presence

Medical images play a vital role in identifying diseases and detecting if organs are functioning correctly. Image processing related to medical images is an active research area in which various techniques are used in order to make diagnosis easier. The brain is a vital organ in our body, and brain tumors are a very critical life altering condition. Identifying tumors is a challenging task and various image processing techniques can be used. Doctors can identify tumors from looking at the scan, and this project attempts to automatically derive these results. In this project, image processing is done for automatically detecting the presence of brain tumors in a given brain scan. Content-based image retrieval extracts features from a query or template image, computes a measure of similarity, and gives results by detecting tumors. Template matching is used to identify a template at any position within the image to identify tumor location. Secondly, early detection of Alzheimer’s, which in turn prevents dementia, can be determined from the presence of amyloid fluid along with the other factors. The amyloid fluid presence helps in detecting dementia at an early stage. The presence of this fluid can be found in a PET scan of the brain. Here, the idea is to show the color distribution from a scan image, i.e., the domination of given colors. Content-based image retrieval’s low level feature based approaches such as color histograms are used. In this project, the conventional K- means algorithm is used for clustering the histograms, and identifying dominant colors

Investigation of new feature descriptors for image search and classification

Content-based image search, classification and retrieval is an active and important research area due to its broad applications as well as the complexity of the problem. Understanding the semantics and contents of images for recognition remains one of the most difficult and prevailing problems in the machine intelligence and computer vision community. With large variations in size, pose, illumination and occlusions, image classification is a very challenging task. A good classification framework should address the key issues of discriminatory feature extraction as well as efficient and accurate classification. Towards that end, this dissertation focuses on exploring new image descriptors by incorporating cues from the human visual system, and integrating local, texture, shape as well as color information to construct robust and effective feature representations for advancing content-based image search and classification. Based on the Gabor wavelet transformation, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, a series of new image descriptors is developed. Specifically, first, a new color Gabor-HOG (GHOG) descriptor is introduced by concatenating the Histograms of Oriented Gradients (HOG) of the component images produced by applying Gabor filters in multiple scales and orientations to encode shape information. Second, the GHOG descriptor is analyzed in six different color spaces and grayscale to propose different color GHOG descriptors, which are further combined to present a new Fused Color GHOG (FC-GHOG) descriptor. Third, a novel GaborPHOG (GPHOG) descriptor is proposed which improves upon the Pyramid Histograms of Oriented Gradients (PHOG) descriptor, and subsequently a new FC-GPHOG descriptor is constructed by combining the multiple color GPHOG descriptors and employing the Principal Component Analysis (PCA). Next, the Gabor-LBP (GLBP) is derived by accumulating the Local Binary Patterns (LBP) histograms of the local Gabor filtered images to encode texture and local information of an image. Furthermore, a novel Gabor-LBPPHOG (GLP) image descriptor is proposed which integrates the GLBP and the GPHOG descriptors as a feature set and an innovative Fused Color Gabor-LBP-PHOG (FC-GLP) is constructed by fusing the GLP from multiple color spaces. Subsequently, The GLBP and the GHOG descriptors are then combined to produce the Gabor-LBP-HOG (GLH) feature vector which performs well on different object and scene image categories. The six color GLH vectors are further concatenated to form the Fused Color GLH (FC-GLH) descriptor. Finally, the Wigner based Local Binary Patterns (WLBP) descriptor is proposed that combines multi-neighborhood LBP, Pseudo-Wigner distribution of images and the popular bag of words model to effectively classify scene images. To assess the feasibility of the proposed new image descriptors, two classification methods are used: one method applies the PCA and the Enhanced Fisher Model (EFM) for feature extraction and the nearest neighbor rule for classification, while the other method employs the Support Vector Machine (SVM). The classification performance of the proposed descriptors is tested on several publicly available popular image datasets. The experimental results show that the proposed new image descriptors achieve image search and classification results better than or at par with other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Context Aware Topic Model (CA-TM), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (Sc-SPM), the Kernel Codebook (KC) and the LBP