A Bayesian Network Model for Relevance Feedback in Image Retrieval

J. Xin, J.S. Jin, and X. Shao (Australia)

Keywords

Contentbased image retrieval, relevance feedback, relevant image adoption, Bayesian networks

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. By performing the process of relevant image adoption, the knowledge accumulated from previous feedback iterations is reasonably incorporated and the chosen feedback information can better capture user's information need. Experimental results on real-world image collections validate the efficacy of our proposed approach.

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