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Approximated Classification in Interactive Facial Image Retrieval

  • Zhirong Yang
  • Jorma Laaksonen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

For databases of facial images, where each subject is depicted in only one or a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A potential way to address this problem is to employ automatic even though imperfect classification on the images according to some high level concepts. In this paper we point out that significant improvement in terms of the occurrence of the first subject hit is feasible only when the classifiers are of sufficient accuracy. In this work Support Vector Machines (SVMs) are incorporated in order to obtain high accuracy for classifying the imbalanced data. We also propose an automatic method to choose the penalty factor of training error and the width parameter of the radial basis function used in training the SVM classifiers. More significant improvement in the speed of retrieval is feasible with small classes than with larger ones. The results of our experiments suggest that the first subject hit can be obtained two to five times faster for semantic classes such as “black persons” or “eyeglass-wearing persons”.

Keywords

Support Vector Machine Facial Image Relevance Feedback Relevant Image Imbalanced Data 
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.

References

  1. 1.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  2. 2.
    Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46(1-3), 131–159 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Classification, Document available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  4. 4.
    ISO/IEC. Information technology - Multimedia content description interface - Part 3: Visual. 15938-3:2002(E)Google Scholar
  5. 5.
    Laaksonen, J., Koskela, M., Oja, E.: PicSOM—self-organizing image retrieval with MPEG-7 content descriptors. IEEE Transactions on Neural Network 13(4), 841–853 (2002)CrossRefGoogle Scholar
  6. 6.
    Lee, J.-H.: Model selection of the bounded SVM formulation using the RBF kernel. Master’s thesis, Department of Computer Science and Information Engineering, National Taiwan University (2001)Google Scholar
  7. 7.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J 16(5), 295–306 (1998)CrossRefGoogle Scholar
  8. 8.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  9. 9.
    Yang, Z., Laaksonen, J.: Interactive retrieval in facial image database using Self-Organizing Maps. In: Proc. of IAPR Conference on Machine Vision Applications (MVA 2005), Tsukuba Science City, Japan (May 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zhirong Yang
    • 1
  • Jorma Laaksonen
    • 1
  1. 1.Laboratory of Computer and Information ScienceHelsinki University of TechnologyEspooFinland

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