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Proceedings Paper

Improving image retrieval performance by integrating long-term learning with short-term learning
Author(s): JunWei Han; Lei Guo
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Paper Abstract

Content-based image retrieval has become an active research topic for more than one decade. Nevertheless, current image retrieval systems still have major difficulties bridging the gap between the user’s implied concept and the low-level image description. To address the difficulties, this paper presents a novel image retrieval model integrating long-term learning with short-term learning. This model constructs a semantic image link network by long-term learning which simply accumulates previous users’ relevance feedback. Then, the semantic information learned from long-term learning process guides short-term learning of a new user. The image retrieval is based on a seamless joint of both long-term learning and short-term learning. The model is easy to implement and can be efficiently applied to a practical image retrieval system. Experimental results on 10,000 images demonstrate that the proposed model is promising.

Paper Details

Date Published: 10 January 2003
PDF: 12 pages
Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); doi: 10.1117/12.476252
Show Author Affiliations
JunWei Han, Northwestern Polytechnical Univ. (China)
Lei Guo, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 5021:
Storage and Retrieval for Media Databases 2003
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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