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Localized Content-Based Image Retrieval Using Semi-Supervised Multiple Instance Learning

  • Dan Zhang
  • Zhenwei Shi
  • Yangqiu Song
  • Changshui Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

In this paper, we propose a Semi-Supervised Multiple-Instance Learning (SSMIL) algorithm, and apply it to Localized Content-Based Image Retrieval(LCBIR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art algorithms is promising.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dan Zhang
    • 1
  • Zhenwei Shi
    • 2
  • Yangqiu Song
    • 1
  • Changshui Zhang
    • 1
  1. 1.State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation,Tsinghua University, Beijing 100084China
  2. 2.Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083P.R. China

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