Topic
Content-based image retrieval
About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.
Papers published on a yearly basis
Papers
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TL;DR: An attempt to present Content Based Image Retrieval (CBIR) system developed for retrieving diseased leaves of soybean using color, shape and texture features of leaf and it is found that when LGGP is combined with color histogram and SIFT retrieval precision is improved.
Abstract: This research paper is an attempt to present Content Based Image Retrieval (CBIR) system developed for retrieving diseased leaves of soybean. It uses color, shape and texture features of leaf. Color features are extracted using HSV color histogram. Scale Invariant Feature Transform (SIFT) provides shape features in the form of matching key points. Local Binary Pattern (LBP) and Gabor filter are widely used texture features. Novel texture feature named Local Gray Gabor Pattern (LGGP) is proposed by combining LBP and Gabor. Performance of all these features with respect to retrieval precision is tested for three soybean leaf diseases. Further color, shape and texture features are combined to increase performance. It is found that when LGGP is combined with color histogram and SIFT retrieval precision is improved. Retrieval efficiency of about 96%, 68% and 76% is achieved for soybean leaves affected by mosaic virus, septoria brown spot and pod mottle disease respectively. Average retrieval efficiency of 80% (for the top 5 retrieval) and 72% (for the top 10 retrieval) is obtained by combined features. This retrieval precision is database dependent and varies with size of the database and quality of images.
64 citations
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04 Jan 1998TL;DR: The use of the color correlogram is suggested as a generic indexing tool to tackle various computer vision problems, and a technique is provided to cut down the storage requirement of correlograms so that it is the same as that of histograms.
Abstract: We suggest the use of the color correlogram as a generic indexing tool to tackle various computer vision problems. Correlograms were shown to be very effective for content-based image retrieval. We adapt the correlogram to handle the problems of image subregion querying, object localization, object tracking, and cut detection. Experimental results suggest that the color correlogram is much more effective than the histogram for these applications, with insignificant additional computational, storage, or processing cost. We also provide a technique to cut down the storage requirement of correlograms so that it is the same as that of histograms, with only negligible performance penalty compared to the original correlogram.
64 citations
01 Jan 1999
TL;DR: This article provides a framework to describe and compare content-based image retrieval systems, in terms of the following technical aspects: querying, relevance feedback, result presentation, features, and matching.
Abstract: This article provides a framework to describe and compare content-based image retrieval systems. Sixteen contemporary systems are described in detail, in terms of the following technical aspects: querying, relevance feedback, result presentation, features, and matching. For a total of 44 systems we list the features that are used. Of these systems, 35 use any kind of color features, 28 use texture, and only 25 use shape features.
64 citations
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21 Nov 1995TL;DR: A self-organizing framework for content-based retrieval of images from large image databases at the object recognition level using the theories of optimal projection for optimal feature selection and a hierarchical image database for rapid retrieval rates is described.
Abstract: We describe a self-organizing framework for content-based retrieval of images from large image databases at the object recognition level. The system uses the theories of optimal projection for optimal feature selection and a hierarchical image database for rapid retrieval rates. We demonstrate the query technique on a large database of widely varying real-world objects in natural settings, and show the applicability of the approach even for large variability within a particular object class.
64 citations
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TL;DR: A content-based mammogram retrieval system, which allows medical professionals to seek mass lesions that are pathologically similar to a given example, and which can achieve the highest precision among all mass lesion types.
64 citations