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Proceedings Paper

Object-based image retrieval through learning from user search patterns and profiles
Author(s): Yaowu Xu; Eli S. Saber; A. Murat Tekalp
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Paper Abstract

In this paper, we propose a self-learning content-based image indexing and retrieval system. Our system employs a hierarchical content representation and a hierarchical content matching method for effective and efficient image/object retrieval. the 'learning' behavior is enabled by our proposed hierarchical content representation which allows easy storage of combinations of regions that have resulted in successful matches to objects of interest as determined by user search patterns and profiles. The learning step effectively performs an automatic analysis of database images into meaningful objects given certain user search patterns and interest profiles. The advantages of the proposed hierarchical content representation and 'learning' schemes are demonstrated on a collection of car and face images, where the significant improvements in search and retrieval speed are described both theoretically and experimentally.

Paper Details

Date Published: 23 December 1999
PDF: 9 pages
Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); doi: 10.1117/12.373554
Show Author Affiliations
Yaowu Xu, Univ. of Rochester (United States)
Eli S. Saber, Univ. of Rochester and Xerox Corp. (United States)
A. Murat Tekalp, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 3972:
Storage and Retrieval for Media Databases 2000
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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