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Proceedings Paper

Self-adaptive relevance feedback based on multilevel image content analysis
Author(s): Yongying Gao; Yujin Zhang; Yu Fu
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Paper Abstract

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Paper Details

Date Published: 1 January 2001
PDF: 11 pages
Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); doi: 10.1117/12.410955
Show Author Affiliations
Yongying Gao, Tsinghua Univ. (China)
Yujin Zhang, Tsinghua Univ. (China)
Yu Fu, Optical Memory National Engineering Research Ctr. (China)

Published in SPIE Proceedings Vol. 4315:
Storage and Retrieval for Media Databases 2001
Minerva M. Yeung; Chung-Sheng Li; Rainer W. Lienhart, Editor(s)

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