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Proceedings Paper

Feature transformation in compressed domain for content-based image retrieval
Author(s): Hau-San Wong; Horace H.S. Ip; Chun-Ip Chiu
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Paper Abstract

This paper addresses the problem of image content characterization in the compressed domain for the purpose of facilitating similarity matching in a multimedia database. Specifically, given the disparity of the content characterization power of compressed domain approaches and those based on pixel-domain features, with the latter being usually considered as the more superior one, our objective is to transform the selected set of compressed domain feature histograms in such a way that the retrieval result based on these features is compatible with their spatial domain counterparts. Since there are a large number of possible transformations, we adopt a genetic algorithm approach to search for the optimal one, where each of the binary strings in the population represents a candidate transformation. The fitness of each transformation is defined as a function of the discrepancies between the spatial-domain and compressed-domain retrieval results. In this way, the GA mechanism ensures that transformations which best approximate the performance of spatial domain retrieval will survive into the next generation and are allowed through the operations of crossover and mutation to generate variations of themselves to further improve their performances.

Paper Details

Date Published: 7 May 2003
PDF: 9 pages
Proc. SPIE 5022, Image and Video Communications and Processing 2003, (7 May 2003); doi: 10.1117/12.476501
Show Author Affiliations
Hau-San Wong, City Univ. of Hong Kong (China)
Horace H.S. Ip, City Univ. of Hong Kong (China)
Chun-Ip Chiu, City Univ. of Hong Kong (China)

Published in SPIE Proceedings Vol. 5022:
Image and Video Communications and Processing 2003
Bhaskaran Vasudev; T. Russell Hsing; Andrew G. Tescher; Touradj Ebrahimi, Editor(s)

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