Human behaviour consistent relevance feedback model for image retrieval

Authors:
Jing Liu;Zhiwei Li;Mingjing Li;Hanqing Lu;Songde Ma
Affiliations:
Chinese Academy of Sciences, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Chinese Academy of Sciences, Beijing, China;Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 15th international conference on Multimedia
Year:
2007

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Abstract

Due to the well known semantic gap, content based image retrieval is a difficult problem. To bridge it, relevance feedback as an effective solution has been extensively studied in literatures. However, existing methods follow a single-line searching philosophy, which may lead to a local optimum in search space. To address the problem, we propose a human behavior consistent relevance feedback model for image retrieval in this paper. Simulating human behaviors, the proposed model enable the user to perform relevance feedback in three manners: Follow up, Go back, and Restart. Each manner is a way for the user to provide the system with his or her opinions about search results. The accumulated feedback information can be used to refine the user query and regulate the similarity metric. We adopt the graph ranking algorithm to model the retrieval process. Experiments conducted on standard Corel dataset and Pascal VOC 2006 dataset demonstrate the effectiveness of the proposed mechanism.