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  • 标题: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
  • 本地全文:下载
  • 作者:趙 剛 ; 小林 亜樹 ; 酒井 善則
  • 期刊名称:映像情報メディア学会誌
  • 印刷版ISSN:1342-6907
  • 电子版ISSN:1881-6908
  • 出版年度:2005
  • 卷号:59
  • 期号:6
  • 页码:884-893
  • DOI:10.3169/itej.59.884
  • 出版社:The Institute of Image Information and Television Engineers
  • 摘要: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.
  • 关键词:対話型画像検索;多グループフィードバック;ラフ集合論;縮約
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