Group-based Relevance Feedback for Interactive Image Retrieval

G. Zhao, A. Kobayashi, and Y. Sakai (Japan)


interactive image retrieval, group-based relevance feed back, rough set theory, reduct


In the present paper, we propose an interactive image re trieval system that uses a novel relevance feedback method called group-based relevance feedback. In the proposed system, the user divides the relevant images into multiple groups according to his/her perspectives, based on which, the retrieval intention of the user will be captured more ac curately than is possible by conventional methods, which use only images for retrieval. Moreover, the retrieval re sults are shown as grouped images, which facilitates the understanding of the user as to why such results were pro duced by the system. In order to implement the proposed system, we also introduce an efficient learning method that uses the reduct from rough set theory to learn the retrieval intention of the user. Finally, retrieval results are presented in order to demonstrate the effectiveness of the newly pro posed system and learning method.

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