Content-based image retrieval using growing hierarchical self-organizing quadtree map
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 707-722 |
| Journal / Publication | Pattern Recognition |
| Volume | 38 |
| Issue number | 5 |
| Publication status | Published - May 2005 |
Link(s)
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.
In: Pattern Recognition, Vol. 38, No. 5, 05.2005, p. 707-722.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
