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Biased Minimax Probability Machine Active Learning for Relevance Feedback in Content-Based Image Retrieval

  • Xiang Peng
  • Irwin King
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

In this paper we apply Biased Minimax Probability Machine (BMPM) to address the problem of relevance feedback in Content-based Image Retrieval (CBIR). In our proposed methodology we treat relevance feedback task in CBIR as an imbalanced learning task which is more reasonable than traditional methods since the negative instances largely outnumber the positive instances. Furthermore we incorporate active learning in order to improve the framework performance, i.e., try to reduce the number of iterations used to achieve the optimal boundary between relevant and irrelevant images. Different from previous works, this model builds up a biased classifier and achieves the optimal boundary using fewer iterations. Experiments are performed to evaluate the efficiency of our method, and promising experimental results are obtained.

Keywords

Image Retrieval Relevance Feedback Relevant Image Optimal Boundary 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 2006

Authors and Affiliations

  • Xiang Peng
    • 1
  • Irwin King
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatin, N.T., Hong Kong

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