Content-based image retrieval using growing hierarchical self-organizing quadtree map |
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Authors: | Sitao Wu [Author Vitae]Author Vitae] Tommy WS Chow [Author Vitae] |
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Affiliation: | Department of Electronic Engineering, City University of Hong Kong, Hong Kong |
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Abstract: | In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system. |
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Keywords: | Content-based image retrieval Growing hierarchical self-organizing quadtree map Image distance Relevance feedback |
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