Relevance feedback for content-based image retrieval using Bayesian network

Authors:
Jing Xin;Jesse S. Jin
Affiliations:
University of New South Wales, NSW, Australia;University of Sydney, NSW, Australia
Venue:
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
Year:
2004

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Abstract

Relevance feedback is a powerful query modification technique in the field of content-based image retrieval. The key issue in relevance feedback is how to effectively utilize the feedback information to improve the retrieval performance. This paper presents a relevance feedback scheme using Bayesian network model for feedback information adoption. Relevant images during previous iterations are reasonably incorporated into the current iteration and the chosen relevant images can better capture user's information need.