This paper addresses the problem of local histogram-based image feature selection for learning binary classifiers. We show a novel technique which efficiently combines histogram feature projection with the conditional mutual information (CMI) based classifier selection scheme. Moreover, we investigate cost-sensitive modifications of the CMI-based selection procedure, which further improves the classification performance. Extensive evaluations show that the proposed methods are suitable for object detection and recognition tasks.


classifier selection mutual information histogram feature 


  1. 1.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  3. 3.
    Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York Inc. (1995)Google Scholar
  4. 4.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory (1995)Google Scholar
  5. 5.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  6. 6.
    Fleuret, F.: Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research 5, 1531–1555 (2004)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Shan, C., Gong, S., McOwan, P.W.: Conditional mutual information based boosting for facial expression recognition. In: British Machine Vision Conference (2005)Google Scholar
  8. 8.
    Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7(2), 179–188 (1936)Google Scholar
  9. 9.
    Wang, H., Li, P., Zhang, T.: Histogram feature-based Fisher linear discriminant for face detection. Neural Computing and Applications 17(1), 49–58 (2008)CrossRefGoogle Scholar
  10. 10.
    Laptev, I.: Improving object detection with boosted histograms. Image and Vision Computing 27(5), 535–544 (2009)CrossRefGoogle Scholar
  11. 11.
    Morik, K., Brockhausen, P., Joachims, T.: Combining statistical learning with a knowledge-based approach – A case study in intensive care monitoring. In: International Conference on Machine Learning (1999)Google Scholar
  12. 12.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  13. 13.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar
  14. 14.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. International Journal of Computer Vision 38(1), 15–33 (2000)zbMATHCrossRefGoogle Scholar
  15. 15.
    García, V., Mollineda, R.A., Sánchez, J.: Theoretical analysis of a performance measure for imbalanced data. In: International Conference on Pattern Recognition (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ákos Utasi
    • 1
  1. 1.Computer Automation Research InstituteHungarian Academy of SciencesBudapestHungary

Personalised recommendations