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Báo cáo hóa học: "Research Article Color-Based Image Retrieval Using Perceptually Modified Hausdorff Distance"

Tham khảo luận văn - đề án 'báo cáo hóa học: "research article color-based image retrieval using perceptually modified hausdorff distance"', luận văn - báo cáo phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 263071 10 pages doi 10.1155 2008 263071 Research Article Color-Based Image Retrieval Using Perceptually Modified Hausdorff Distance Bo Gun Park Kyoung Mu Lee and Sang Uk Lee Department of Electrical Engineering ASRI Seoul National University Seoul 151-742 South Korea Correspondence should be addressed to Kyoung Mu Lee kyoungmu@snu.ac.kr Received 31 July 2007 Accepted 22 November 2007 Recommended by Alain Tremeau In most content-based image retrieval systems the color information is extensively used for its simplicity and generality. Due to its compactness in characterizing the global information a uniform quantization of colors or a histogram has been the most commonly used color descriptor. However a cluster-based representation or a signature has been proven to be more compact and theoretically sound than a histogram for increasing the discriminatory power and reducing the gap between human perception and computer-aided retrieval system. Despite of these advantages only few papers have broached dissimilarity measure based on the cluster-based nonuniform quantization of colors. In this paper we extract the perceptual representation of an original color image a statistical signature by modifying general color signature which consists of a set of points with statistical volume. Also we present a novel dissimilarity measure for a statistical signature called Perceptually Modified Hausdorff Distance PMHD that is based on the Hausdorff distance. In the result the proposed retrieval system views an image as a statistical signature and uses the PMHD as the metric between statistical signatures. The precision versus recall results show that the proposed dissimilarity measure generally outperforms all other dissimilarity measures on an unmodified commercial image database. Copyright 2008 Bo Gun Park et al. This is an open access article distributed under the Creative Commons