Top PDF Global Descriptor Attributes Based Content Based Image Retrieval of Query Images

Global Descriptor Attributes Based Content Based Image Retrieval of Query Images

Global Descriptor Attributes Based Content Based Image Retrieval of Query Images

There are different models for color image representation. In the seventeen century Sir Isaac Newton showed that a beam of sunlight passing through a glass prism comes into view as a rainbow of colors. Therefore, he first understood that white light is composed of many colors[6]. Typically, the computer screen can display 2^8 or 256 different shades of gray. For color images this makes 2^ (3x8) = 16,777,216 different colors. Clerk Maxwell showed in the late nineteen century that every color image cough be created using three images – Red, green and Blue image. A mix of these three images can produce every color. This model, named RGB model, is primarily used in image representation. The RGB image could be presented as a triple(R, G, B) where usually R, G, and B take values in the range [0, 255]. Another color model is YIQ model (lamination (Y), phase (I), quadrature phase (Q)). It is the base for the color television standard. Images are presented in computers as a matrix of pixels. They have finite area. If we decrease the pixel dimension the pixel brightness will become close to the real brightness. A content-based image retrieval system (CBIR) is a piece of software that implements CBIR. In CBIR, each image that is stored in the database has its features extracted and compared to the features of the query image[7].
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A Refined Hybrid Image Retrieval System using Text and Color

A Refined Hybrid Image Retrieval System using Text and Color

The proposed IR system uses both the visual content and text (metadata) associated with the image. The system has user interface where user can enter the query in the form of text and/or a query image. The text and image feature descriptor vectors are generated for query text and image as well as the images in Database. The similarity matching algorithm is executed on the descriptors vectors and the two independent lists of score vectors are generated for the query text and query image with different scores. Now these two lists are combined in a significant way to give user the combined score list of images. The default weight given to both the methods is 0.5(50%).The relevant set of images are retrieved based upon the threshold value set. They are arranged in decreasing order of their relevance. Results obtained are filtered again using color histogram to remove unwanted images. Fig. 1 shows the flowchart for the proposed system.
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MammoSys: A content-based image retrieval system using breast density patterns

MammoSys: A content-based image retrieval system using breast density patterns

Images can be numerically represented by a feature vec- tor, preferentially at a low-dimensional space in which the most relevant visual aspects are emphasized [15,16]. Visu- ally, breasts of fatty and dense densities differ by gray level intensities in mammographies, as can be seen in Fig. 1. In order to describe the different patterns of parenchyma tis- sue within one category, the texture attribute can be used, since texture contains information about the spatial distri- bution of gray levels and variations in brightness, turning the representation of breast density appropriate [17]. However, the high dimensionality of a feature vector that represents texture attributes limits its computational efficiency, so it is desirable to choose a technique that combines the representation of the texture with the reduction of dimensionality, in a way to turn the retrieval algorithm more effective and computationally treatable. The two-dimensional principal component analysis (2DPCA) technique is able to satisfy these requirements.
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Text and Content Based Image Retrieval Via Locality Sensitive Hashing

Text and Content Based Image Retrieval Via Locality Sensitive Hashing

Abstract—We present a scalable image retrieval system based jointly on text annotations and visual content. Previous ap- proaches in content based image retrieval often suffer from the semantic gap problem and long retrieving time. The solution that we propose aims at resolving these two issues by indexing and retrieving images using both their text descriptions and visual content, such as features in colour, texture and shape. A query in this system consists of keywords, a sample image and relevant parameters. The retrieving algorithm first selects a subset of images from the whole collection according to a comparison between the keywords and the text descriptions. Visual features extracted from the sample image are then compared with the extracted features of the images in the subset to select the closest. Because the features are represented by high-dimensional vectors, locality sensitive hashing is applied to the visual comparison to speedup the process. Experiments were performed on a collection of 1514 images. The timing results showed the potential of this solution to be scaled up to handle large image collections.
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Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words

Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words

With advances in the multimedia technologies and the social networks, CBIR is considered an active research topic. Recent CBIR systems rely on the use of the BoVW model for being enables efficient indexing for local image features. This paper presented a system for CBIR, which uses local feature descriptors to produce image signatures that are invariant to rotation and scale. The system combines the robust techniques, such as SIFT, SURF, and BoVW, to enhance the retrieval process. In the system, we used a k-means algorithm to cluster the feature descriptors in order build a visual vocabulary. As well as, SVM is used as a classifier model to retrieve much more images relevant to the query efficiently in the features space.
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Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

Content-based image retrieval using spatial layout information in brain tumor T1-weighted contrast-enhanced MR images.

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.
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A Novel Content-Based Image Retrieval Technique Using Tree Matching

A Novel Content-Based Image Retrieval Technique Using Tree Matching

CBIR system extracts important visual features of an image, then generates a vector based on the image features, called feature vector and calculates the similarity of feature vector, extracted from query image, and other images stored in the database. Finally, it presents and ranks a sequence of similar images to the query one, based on a similarity measurement. There are two groups of CBIR system: histogram and region based methods. The histogram-based methods extract global features, such as color, texture and shape while the region-based CBIR systems segment images into some specific regions and retrieves images based on the similarity between them.
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Content Based Image Retrieval by Multi Features using Image Blocks

Content Based Image Retrieval by Multi Features using Image Blocks

Content based image retrieval (CBIR) is an effective method of retrieving images from large image resources. CBIR is a technique in which images are indexed by extracting their low level features like, color, texture, shape, and spatial location, etc. Effective and efficient feature extraction mechanisms are required to improve existing CBIR performance. This paper presents a novel approach of CBIR system in which higher retrieval efficiency is achieved by combining the information of image features color, shape and texture. The color feature is extracted using color histogram for image blocks, for shape feature Canny edge detection algorithm is used and the HSB extraction in blocks is used for texture feature extraction. The feature set of the query image are compared with the feature set of each image in the database. The experiments show that the fusion of multiple features retrieval gives better retrieval results than another approach used by Rao et al. This paper presents comparative study of performance of the two different approaches of CBIR system in which the image features color, shape and texture are used.
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Content based image retrieval based on eye physiological structure and relevance feedback

Content based image retrieval based on eye physiological structure and relevance feedback

Content Based Image Retrieval (CBIR) includes a set of methods for processing visual features of a query image to find similar images at an images database. As extracting features and determining similarity measure, are two main stages in retrieval systems. In this paper we have tried to give weight to image pixels and suggest an effective feature vector by use of eye physiological structure and annotation issue. Then modify feature components weight and optimize similarity measure by use of information of relative and irrelative images in each feedback. So by means of annotation issue in physiological structure of eye, pixels in center of image are more important and have more effective role in extracted features and also system accuracy will be increased for determining similar images, by means of optimizing similarity measure in each stage.
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AN EFFICIENT CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE OF IMAGE SUBBLOCKS

AN EFFICIENT CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE OF IMAGE SUBBLOCKS

Image retrieval is an active research area in image processing, pattern recognition, and computer vision. For the purpose of effectively retrieving more similar images from the digital image databases, this paper uses the local HSV color and Gray level co-occurrence matrix (GLCM) texture features. The image is divided into sub blocks of equal size. Then the color and texture features of each sub-block are computed. Color of each sub-block is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by cumulative color histogram. Texture of each sub-block is obtained by using gray level co-occurrence matrix. An integrated matching scheme based on Most Similar Highest Priority (MSHP) principle is used to compare the query and target image. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image. This matrix is used for matching the images. Euclidean distance measure is used in retrieving the similar images. As the experimental results indicated, the proposed technique indeed outperforms other retrieval schemes in terms of average precision.
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Query By Image Content using Discrete Cosine Transform

Query By Image Content using Discrete Cosine Transform

There are various approaches for QBIC. One of the most popular methods of QBIC is image retrieval based on feature extraction. Image retrieval is an amalgamation of low set of features and high set of features. Color, shape and texture belong to low level features. High level features cannot be extracted from visual contents. It uses conventional textual descriptor for image retrieval. Fundamental technique of QBIC is visual feature extraction. This is mainly to overcome the semantic gap which is the difference between the interpretation of the user and the information in the visual data. Features that are widely used include spatial relationships, shape, color, texture.
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Comparison Contour Extraction Based on Layered Structure and Fourier Descriptor on Image Retrieval

Comparison Contour Extraction Based on Layered Structure and Fourier Descriptor on Image Retrieval

The Content-Based Image Retrieval (CBIR) technique uses image content to search and retrieve digital images. Basically, CBIR systems try to retrieve images similar to a user-defined specification or pattern (e.g., shape sketch, image example). Their goal is to support image retrieval based on content properties (e.g., shape, color, texture) [1], CBIR is also based on the idea of extracting visual features from the image and using them to index images in a database. Content- based image retrieval systems were introduced to address the problems associated with text-based image retrieval. CBIR is a set of techniques for retrieving semantically-relevant images from an image database based on automatically-derived image features[2]. Content-based image retrieval also known as query by image content is a technique which uses visual content that well known as features for extracting similar images from an image in a database[3][4][5]. Image database every time become bigger and it makes a problem dealing with database organization so the necessity of efficient algorithm is obvious needed [6].
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Diversification in an image retrieval system based on text and image processing

Diversification in an image retrieval system based on text and image processing

Over time, various theories involving search results diversification have been developed, theories that have been taken into consideration [1]: (i) content [2], i.e. how different are the results to each other, (ii) novelty [3], [4], i.e. what does the new result offer in addition to the previous ones, and (iii) semantic coverage [5], i.e. how well covered are the different interpretations of the user query. In the MUCKE project, we create a collection with over 80 millions images with their associ- ated metadata, mainly extracted from Flickr 3

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Image Information Retrieval: An Overview of Current Research

Image Information Retrieval: An Overview of Current Research

Several problems remain including retrieval of features based on location within an image, the extension of 2-dimensional features to 3-dimension, and appropriate segmentation of video images. Although research in higher order CBIR is un- derway, current systems are not capable of retrieving all in- stances of horses based on the shape, color, or texture of a single instance of a horse in a query. For example, a shape- based query depicting a side view of a horse does not retrieve images of horses from behind or above. Research in object recognition conducted by Forsythe et al (1997) has sought to develop techniques for modeling a class of objects and identi- fying defining attributes and features for that class. Rorvig has examined the use of human judgments to train the system to recognize patterns of user-defined similarity for automatic identification of image classes. Chang et al, (1998) also util- ized users' relevance judgments to refine searches and to as- sign semantic keywords to images that can be used by subse- quent users to query the system.
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A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

Content Based Image Retrieval (CBIR) has become an important area of research with the ever increasing demand and use of digital images in various fields such as medicine, engineering, sciences, digital photography etc. Unlike the traditional method of text-based image retrieval in which the image search is based on textual description associated with the images, CBIR systems retrieve images based on the content of the image such as colour, texture, shape or any other information that can be automatically extracted from the image itself and using it as a criterion to retrieve content related images from the database. The retrieved images are then ranked according to the relevance between the query image and images in the database in proportion to a similarity measure calculated from the features [1][2][3].
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PERFORMANCE EVALUATION OF CONTENT BASED IMAGE RETRIEVAL FOR MEDICAL IMAGES

PERFORMANCE EVALUATION OF CONTENT BASED IMAGE RETRIEVAL FOR MEDICAL IMAGES

J48 is slightly modified C4.5 in WEKA. The C4.5 algorithm generates a classification-decision tree for a specific data-set through recursive data partitioning. Decision tree is grown using Depth-first strategy. The algorithm considers all tests that split a data set and selects a test that leads to best information gain. For every discrete attribute, one test with outcomes as many as distinct attribute values is considered. Binary tests involving every distinct values of the attribute are considered for each continuous attribute. To gather all binary tests entropy gain efficiently training data set of the node being considered is sorted for continuous attribute values with entropy gains of binary cut based on each distinct values being calculated in one sorted data scan, this process being repeated for every continuous attributes [17].
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A Novel Approach for Information Content Retrieval and Analysis of Bio-Images using Datamining techniques

A Novel Approach for Information Content Retrieval and Analysis of Bio-Images using Datamining techniques

In Bio-Medical image processing domain, content- based analysis and Information retrieval of bio- images is very critical for disease diagnosis. Content-Based Image Analysis and Information Retrieval (CBIAIR) has become a significant part of information retrieval technology. One challenge in this area is that the ever-increasing number of bio-images acquired through the digital world makes the brute force searching almost impossible. Medical Image structural objects content and object identification plays significant role for image content analysis and information retrieval. There are basically three fundamental concepts for content-based bio-image retrieval, i.e. visual- feature extraction, multi-dimensional indexing, and retrieval system process. Each image has three contents such as: colour, texture and shape features. Colour and Texture both plays important image visual features used in Content-Based Image Retrieval to improve results. In this paper, we have presented an effective image retrieval system using features like texture, shape and color, called CBIAIR (Content-Based Image Analysis and Information Retrieval). Here, we have taken three different features such as texture, color and shape. Firstly, we have developed a new texture pattern feature for pixel based feature in CBIAIR system. Subsequently, we have used semantic color feature for color based feature and the shape based feature selection is done using the existing technique. For retrieving, these features are extracted from the query image and matched with the feature library using the feature weighted distance. After that, all feature vectors will be stored in the database using indexing procedure. Finally, the relevant images that have less matched distance than the predefined threshold value are retrieved from the image database after adapting the K-NN classifier.
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Image Information Retrieval From Incomplete Queries Using Color and Shape Features

Image Information Retrieval From Incomplete Queries Using Color and Shape Features

An effective, working and efficient shape descriptor is a key component of content description for an image, since shape is a basic property of an object present in the image itself [10]. These shape descriptors are broadly categorized into two groups, i.e., contour-based shape descriptors and region based shape descriptors. Due to the fact that contour-based shape descriptors exploit only boundary information, they cannot capture the interior shape of the objects and also these methods cannot deal with disjoint shapes or the shape which is not closed where contour information is not available. In region based techniques, shape descriptors are derived using all the pixel information within a shape region. Region-based shape descriptors can be applied to general applications [6]. Fourier Descriptor (FD) and Moment Invariants are used for shape analysis since they are perfect shape descriptor and do not produce redundant values. Since the colored image has several kinds of objects, Fourier descriptor may be the best possible descriptor for calculating the boundaries of the objects present in the images. The proposed shape descriptor is derived by applying 2-D Fourier transform on an image. The acquired shape descriptor is application independent and robust. Their main advantages are that they are invariant to translation, rotation and scaling of the observed object. Thus shape description become independent of the relative position and size of the object in the input image [11, 12].
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A Novel Algorithm for Region-Based Image Retrieval Framework

A Novel Algorithm for Region-Based Image Retrieval Framework

In the last decade the number of medical images especially digital images is increasing which is used for diagnosing and therapies. With the wide growth of medical images database, the statistical analysis of medical images is becoming a big challenge. When a physician is studying a case he is interested in more similar cases and the similarity measures usually involves a medical background. The benefit of archiving medical images can be gained only when a good query tool is adapted to allow users to browse the medical images. Content-based image retrieval faces few problems when it is applied on medical images. Two of the major difficulties are:
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Radiol Bras  vol.40 número4

Radiol Bras vol.40 número4

Figure 4 shows the results of the execu- tion of the first module with mean values of precision-recall curves of the Euclidean distance between characteristic vectors of the reference images in relation to the im- ages of the database. This result allowed the evaluation of the CBIR effectiveness utilizing the texture in the classification of the most similar images for the second module. Although the mean precision ob- tained in the experiments is 0.54 (sagittal knee), and 0.40 (axial head), it is sufficient for filtering the images to be submitted to the second module. In the second module, the images are processed with the similar- ity measurement algorithm on the compu- tational grid. CBIR with the similarity measurement algorithm resulted in a satis- factory precision for both anatomical re- gions — 0.95 (sagittal knee) and 0.92 (axial head) —, according to the mean precision- recall curves between the reference images and those classified by the first module (Figure 5).
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