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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Cox, I.J., Miller, M.L., Minka, T.P., Yianilos, P.N.: An optimized interaction strategy for bayesian relevance feedback. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (1998)Google Scholar
  2. 2.
    Giacinto, G., Roli, F.: Bayesian relevance feedback for content-based image retrieval. Pattern Recognition (2004)Google Scholar
  3. 3.
    Hong, P., Tian, Q., Huang, T.S.: Incorporate support vector machines to content-based image retrieval with relevance feedback. In: IEEE International Conference on Image Processing (2000)Google Scholar
  4. 4.
    Huang, K., Yang, H., King, I., Lyu, M.R.: Imbalanced learning with biased minimax probability machine. IEEE Transactions on System, Man, and Cybernetics (2005)Google Scholar
  5. 5.
    Huang, K., Yang, H., King, I., Lyu, M.R., Chan, L.: The minimum error minimax probability machine. Journal of Machine Learning Research 5, 1253–1286 (2004)MathSciNetGoogle Scholar
  6. 6.
    Jain, A.K., Vailaya, A.: Shape-based retrieval: a case study with trademark image database. Pattern Recognition 9, 1369–1390 (1998)CrossRefGoogle Scholar
  7. 7.
    Lanckriet, G., Ghaoui, L., Bhattacharyya, C., Jordan, M.I.: Minimax probability machine. In: Advances in Neural Infonation Processing Systems (2002)Google Scholar
  8. 8.
    Lanckriet, G., Ghaoui, L., Bhattacharyya, C., Jordan, M.I.: A robust minimax approach to classification. Journal of Machine Learning Research 3, 555–582 (2003)MATHCrossRefGoogle Scholar
  9. 9.
    Rui, Y., Huang, T.S., Mehrotra, S.: Content-based image retrieval with relevance feedback in mars. In: IEEE International Conference on Image Processing (1997)Google Scholar
  10. 10.
    Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content–based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 644–655 (1998)CrossRefGoogle Scholar
  11. 11.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Contentbased image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  12. 12.
    Su, Z., Zhang, H., Ma, S.: Relevance feedback using a bayesian classifier in content-based image retrieval. In: SPIE Electronic Imaging (2001)Google Scholar
  13. 13.
    Yan, R., Hauptmann, A.G., Jin, R.: Negative pseudo-relevance feedback in content-based video retrieval. In: ACM Multimedia (2003)Google Scholar

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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