Content-based image retrieval using growing hierarchical self-organizing quadtree map

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

33 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)707-722
Journal / PublicationPattern Recognition
Volume38
Issue number5
Publication statusPublished - May 2005

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. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Research Area(s)

  • Content-based image retrieval, Growing hierarchical self-organizing quadtree map, Image distance, Relevance feedback

Citation Format(s)

Content-based image retrieval using growing hierarchical self-organizing quadtree map. / Wu, Sitao; Rahman, M. K M; Chow, Tommy W.S.
In: Pattern Recognition, Vol. 38, No. 5, 05.2005, p. 707-722.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review