Imbalanced Learning in Relevance Feedback with Biased Minimax Probability Machine for Image Retrieval Tasks

  • Xiang Peng
  • Irwin King
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


In recent years, Minimax Probability Machine (MPM) have demonstrated excellent performance in a variety of pattern recognition problems. At the same time various machine learning methods have been used on relevance feedback tasks in Content-based Image Retrieval (CBIR). One of the problems in typical techniques for relevance feedback is that they treat the relevant feedback and irrelevant feedback equally. In other words, the negative instances largely outnumber the positive instances. Hence, the assumption that they are balanced is incorrect. In this paper we study how MPM can be applied to image retrieval, more precisely, Biased MPM during the relevance feedback iterations. We formulate the relevance feedback based on a modified MPM called Biased Minimax Probability Machine (BMPM). Different from previous methods, this model directly controls the accuracy of classification of the future data to build up biased classifiers. Hence, it provides a rigorous treatment on imbalanced data. Mathematical formulation and explanations are provided for showing the advantages. Experiments are conducted to evaluate the performance of our proposed framework, in which encouraging and promising experimental results are obtained.


Support Vector Machine Image Retrieval Relevance Feedback Synthetic Dataset Relevant Image 
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 KongShatinHong Kong

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