An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance Feedback
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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 solution.Keywords:
Relevance Feedback
Content-Based Image Retrieval
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Relevance
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There is a serious flaw in existing image search engines, since they basically work under the influence of keywords.Retrieving images based on the keywords is not only inappropriate, but also time consuming.Content Based Image Retrieval (CBIR) is still a research area, which aims to retrieve images based on the content of the query image.In this paper we have proposed a CBIR based image retrieval system, which analyses innate properties of an image such as, the color, texture and the entropy factor, for efficient and meaningful image retrieval.The initial step is to retrieve images based on the color combination of the query image, which is followed by the texture based retrieval and finally, based on the entropy of the images, the results are filtered.The proposed system results in retrieving the images from the database which are similar to the query image.Entropy based image retrieval proved to be quite useful in filtering the irrelevant images thereby improving the efficiency of the system.
Content-Based Image Retrieval
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Image classification is perhaps the most important part of digital image analysis. Retrieval patternbased learning is the most effective that aim to establish the relationship between the current and previous query sessions by analyzing image retrieval patterns. We propose a new feedback based and content based image retrieval system. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this paper we present content-based image retrieval system that uses color and texture as visual features to describe the content of an image region. Our contribution are we use Gabor filters to extract texture features from arbitrary shaped regions separated from an image after segmentation to increase the system effectiveness. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from color the whole image, and features extracted from Texture some image regions. That approach is more effective and efficient way for image retrieval.
Content-Based Image Retrieval
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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 solution.
Relevance Feedback
Content-Based Image Retrieval
Feature (linguistics)
Relevance
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In Bio-Medical image processing domain, contentbased analysis and Information retrieval of bioimages 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. visualfeature 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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With the rapid advancement of digital imaging technologies, and the use of large volume image databases in various applications, it becomes imperative to build an automatic and an efficient image retrieval system. Content Based Image Retrieval (CBIR) is most emerging and vivid research area in computer vision, in which unknown query image assigns to the closest possible similar images available in the database. Current systems mainly use colour, texture, and shape information for image retrieval using similarity measures between query and database images features. Here this work, proposed a classification system that allows recognizing and recovering the class of a query image based on its visual content. This successful categorization of images greatly enhances the performance of retrieval by filtering out irrelevant classes. In this way we have done the comparative analysis of various features as an individual or in combinations, with direct similarity measure and proposed framework. Experimentson benchmark Wang database show that the proposed classification & retrieval framework performs significantly better than the common framework of distances.
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Content-Based Image Retrieval is a process to retrieve the similar images from the huge set of image database corresponding the query image. In CBIR Visual features of the input image are extracted and on the basis of similarity matching algorithm, the required precise image are extracted. To understand the evaluation and evolution of CBIR system various research were studied and various research is going on this way also. In this paper, we have discussed some of the popular pixel level feature extraction techniques for Content-Based Image Retrieval and we also present here about the performance of each technique.
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The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents.CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image.The image descriptors include texture, color, intensity and shape of the object inside an image.Several feature-extraction techniques viz., Average RGB, Color Moments, Cooccurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper.However, individually these techniques result in poor performance.So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query.We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping.It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.
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Content-based image retrieval (CBIR) systems are capable to use query for visually related images by identifying similarity between a query Image and those in the image database. The CBIR Systems can be classified broadly into two classes as Low-level feature based system and High level Semantic feature based system. Image contents are plays significant role for image retrieval. The most common contents are color, texture and shape. An efficient image retrieval system must be based on well-organized image feature extraction. K-means clustering is used to group similar and dissimilar objects in an image database into k disjoint clusters whereas neural network is used as a retrieval engine to measure the overall similarity between the query and the images. Relevance feedback is a query modification technique in the field of content-based image retrieval to improve the retrieval performance.
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Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user's requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time.
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In a content-based image retrieval system (CBIR), the main issue is to extract the image features that effectively represent the image contents in a database.Such an extraction requires a detailed evaluation of retrieval performance of image features.This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features.Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared.The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined.The similarity measures based on which matches are made and images are retrieved are also discussed.For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.
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