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A Rich Get Richer Strategy for Content-Based Image Retrieval

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Advances in Visual Information Systems (VISUAL 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1929))

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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.

The work reported in the paper was performed while the author was an intern at Microsoft Research China, Feb.–Apr. 2000.

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Author information

Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China

    Lijuan Duan1, Wen Gao & Jiyong Ma

Authors
  1. Lijuan Duan1
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  2. Wen Gao
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  3. Jiyong Ma
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Editor information

Editors and Affiliations

  1. Claude Bernard University of Lyon, LISI - 502, INSA de Lyon, 69621, Villeurbanne Cedex, France

    Robert Laurini

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© 2000 Springer-Verlag Berlin Heidelberg

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Duan1, L., Gao, W., Ma, J. (2000). A Rich Get Richer Strategy for Content-Based Image Retrieval. In: Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2000. Lecture Notes in Computer Science, vol 1929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40053-2_26

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  • DOI: https://doi.org/10.1007/3-540-40053-2_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41177-2

  • Online ISBN: 978-3-540-40053-0

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