Adaptive relevance feedback based on Bayesian inference for image retrieval

  • Authors:

  • Lijuan Duan;Wen Gao;Wei Zeng;Debin Zhao

  • Affiliations:

  • The College of Computer Science, Beijing University of Technology, Beijing 100022, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China;Department of Computer Science, Harbin Institute of Technology, Harbin 150001, China

  • Venue:
  • Signal Processing - Special section on content-based image and video retrieval
  • Year:
  • 2005

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Abstract

Relevance feedback can be considered as a Bayesian classification problem. For retrieving images efficiently, an adaptive relevance feedback approach based on the Bayesian inference, rich get richer (RGR), is proposed. If the feedback images in current iteration are consistent with the previous ones, the images that are similar to the query target are assigned to high probabilities. Therefore, the images that are similar to the user's ideal target are emphasized step by step. The experiments showed that the average precision of RGR improves 5-20% on each interaction compared with non-RGR. When compared with MARS. the proposed approach greatly reduces the user's efforts for composing a query and captures user's, intention efficiently