ラフ集合論の縮約による多グループフィードバックを用いた対話型画像検索

抄録

In this paper, we propose an interactive image retrieval 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 perspective. Using each user's perspective, the retrieval intention of the user will be captured more accurately than is possible by conventional methods, which use only images for retrieval. Moreover, the retrieval results are shown as grouped images, which facilitates the understanding of the user as to why such results were produced by the system. In order to implement the proposed system, we also introduce an efficient learning method that uses the “Reduct” from the Rough Set Theory to learn the retrieval intention of the user. Finally, retrieval results are presented in order to demonstrate the effectiveness of the proposed system and learning method.