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Content-Based Image Retrieval By Relevance Feedback

  • Zhong Jin
  • Irwin King
  • Xuequn Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)

Abstract

Relevance feedback is a powerful technique for content-based image retrieval. Many parameter estimation approaches have been proposed for relevance feedback. However, most of them have only utilized information of the relevant retrieved images, and have given up, or have not made great use of information of the irrelevant retrieved images. This paper presents a novel approach to update the interweights of integrated probability function by using the information of both relevant and irrelevant retrieved images. Experimental results have shown the effectiveness and robustness of our proposed approach, especially in the situation of no relevant retrieved images.

Keywords

Image Retrieval Query Image Relevance Feedback Retrieval Performance Invariant Moment 
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

  • Zhong Jin
    • 1
  • Irwin King
    • 2
  • Xuequn Li
    • 2
  1. 1.Department of Computer ScienceNanjing University of Science and TechnologyNanjingPeople’s Republic of China
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong

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