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A Rich Get Richer Strategy for Content-Based Image Retrieval

  • Lijuan Duan1
  • Wen Gao
  • Jiyong Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)

Abstract

A novel relevance feedback approach to image retrieval, Rich Get Richer (RGR) Strategy, is proposed in this paper. It is based on the general framework of Bayesian inference in statistics. The user’s feedback information is propagated into the retrieval process step by step. The more promising images are emphasized by the Rich Get Richer (RGR) Strategy. On the contrary, the less promising ones are de-emphasized. The experimental results show that the proposed approach can capture the user’s information need more precisely. By using RGR, the average precision improves from 5 to 20% for each interaction.

Keywords

Image Retrieval Average Precision Relevance Feedback Image Retrieval System CBIR System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Lijuan Duan1
    • 1
  • Wen Gao
    • 1
  • Jiyong Ma
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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