Advertisement

Towards the Optimal Training of Cascades of Boosted Ensembles

  • S. Charles Brubaker
  • Jianxin Wu
  • Jie Sun
  • Matthew D. Mullin
  • James M. Rehg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4170)

Abstract

Cascades of boosted ensembles have become a popular technique for face detection following their introduction by Viola and Jones. Researchers have sought to improve upon the original approach by incorporating new techniques such as alternative boosting methods, feature sets, etc. We explore several avenues that have not yet received adequate attention: global cascade learning, optimal ensemble construction, stronger weak hypotheses, and feature filtering. We describe a probabilistic model for cascade performance and its use in a fully-automated training algorithm.

Keywords

False Positive Rate Operating Point False Negative Rate Object Detection Face Detection 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blanchard, G., Geman, D.: Sequential testing designs for pattern recognition. Annals of Statistics 33(3), 1155–1202 (2005)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Brubaker, S.C., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M.: On the design of cascades of boosted ensembles for face detection. Technical Report GIT-GVU-05-28, Georgia Institute of Technology (2005)Google Scholar
  3. 3.
    Elad, M., Hel-Or, Y., Keshet, R.: Pattern detection using a maximal rejection classifier. Pattern Recognition Letters 23(12), 1459–1471 (2002)MATHCrossRefGoogle Scholar
  4. 4.
    Fan, W., Stolfo, S.J., Zhang, J., Chan, P.K.: Adacost: Misclassification cost-sensitive boosting. In: Proc. 16th Int’l Conf Machine Learning, pp. 97–105 (1999)Google Scholar
  5. 5.
    Fleuret, F.: Fast binary feature selection with conditional mutual information. J. Mach. Learn. Res. 5, 1531–1555 (2004)MathSciNetGoogle Scholar
  6. 6.
    Froba, B., Ernst, A.: Face detection with the modified census transform. In: 6th IEEE Int’l Conf. Automatic Face and Gesture Recognition, pp. 91–96 (May 2004)Google Scholar
  7. 7.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)MATHGoogle Scholar
  8. 8.
    Gupta, A.K., Nadarajah, S. (eds.): Handbook of Beta Distribution and its applications. Marcel Dekker, Inc., New York (2004)MATHGoogle Scholar
  9. 9.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATHCrossRefGoogle Scholar
  10. 10.
    Heisele, B., Serre, T., Mukherjee, S., Poggio, T.: Feature reduction and hierarchy of classifiers for fast object detection in video images. In: CVPR, vol. II, pp. 18–24 (2001)Google Scholar
  11. 11.
    Huang, K., Yang, H., King, I., Lyu, M.R.: Learning classifiers from imbalanced data based on biased minimax probability machine. In: CVPR, vol.II, pp. 558–563 (2004)Google Scholar
  12. 12.
    Karakoulas, G.J., Shawe-Taylor, J.: Optimizing classifiers for imbalanced training sets. In: NIPS 11, pp. 253–259 (1999)Google Scholar
  13. 13.
    Keren, D., Osadchy, M., Gotsman, C.: Antifaces: A novel, fast method for image detection. IEEE Trans. on PAMI 23(7), 747–761 (2001)Google Scholar
  14. 14.
    Levi, K., Weiss, Y.: Learning object detection from a small number of examples: The importance of good features. In: CVPR, vol.II, pp. 53–60 (2004)Google Scholar
  15. 15.
    Li, S.Z., Zhang, Z.Q.: Floatboost learning and statistical face detection. IEEE Trans. on PAMI 26(9), 1112–1123 (2004)Google Scholar
  16. 16.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Liu, C., Shum, H.-Y.: Kullback-leibler boosting. In: CVPR, vol. II, pp. 587–594 (2003)Google Scholar
  18. 18.
    Luo, H.: Optimization design of cascaded classifiers. In: CVPR, vol.I, pp. 480–485 (2005)Google Scholar
  19. 19.
    Romdhani, S., Torr, P., Schoelkopf, B., Blake, A.: Computationally efficient face detection. In: Proc. ICCV, pp. 695–700 (2001)Google Scholar
  20. 20.
    Schapire, R.E., Singer, Y.: Improved boosting using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999)MATHCrossRefGoogle Scholar
  21. 21.
    Schneiderman, H.: Feature-centric evaluation for efficient cascaded object detection. In: CVPR, vol. II, pp. 29–36 (2004)Google Scholar
  22. 22.
    Sun, J., Rehg, J.M., Bobick, A.F.: Automatic cascade training with perturbation bias. In: CVPR, vol. II, pp. 276–283 (2004)Google Scholar
  23. 23.
    Sung, K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. on PAMI 20(1), 39–51 (1998)Google Scholar
  24. 24.
    Ting, K.M.: A comparative study of cost-sensitive boosting algorithms. In: Proc. 17th Int’l. Conf. Machine Learning, pp. 983–990 (2000)Google Scholar
  25. 25.
    Vasconcelos, N.: Feature selection by maximum marginal diversity: Optimality and implications for visual recognition. In: CVPR, vol.1, pp. 762–772 (2003)Google Scholar
  26. 26.
    Vidal-Naquet, M., Ullman, S.: Object recognition with informative features and linear classification. In: Proc. ICCV, pp. 281–288 (2003)Google Scholar
  27. 27.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: NIPS 14, pp. 1311–1318 (2002)Google Scholar
  28. 28.
    Viola, P., Jones, M.J.: Robust real-time object detection. Technical Report CRL 2001/01, Compaq Cambridge Research Laboratory (February 2001)Google Scholar
  29. 29.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  30. 30.
    Wu, J., Brubaker, S.C., Mullin, M.D., Rehg, J.M.: Fast asymmetric learning for cascade face detection. Technical Report GIT-GVU-05-27, Georgia Institute of Technology (2005)Google Scholar
  31. 31.
    Wu, J., Mullin, M., Rehg, J.: Linear asymmetric classifier for cascade detectors. In: Proc. 22nd Int’l Conf. Machine Learning, pp. 993–1000 (2005)Google Scholar
  32. 32.
    Wu, J., Rehg, J.M., Mullin, M.D.: Learning a rare event detection cascade by direct feature selection. In: NIPS 16, pp. 1523–1530 (2004)Google Scholar
  33. 33.
    Xiao, R., Zhu, L., Zhang, H.-J.: Boosting chain learning for object detection. In: Proc. ICCV, vol. 1, pp. 709–715 (2003)Google Scholar
  34. 34.
    Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans. on PAMI 24(1), 34–58 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. Charles Brubaker
    • 1
  • Jianxin Wu
    • 1
  • Jie Sun
    • 1
  • Matthew D. Mullin
    • 1
  • James M. Rehg
    • 1
  1. 1.Georgia Institute of TechnologyCollege of Computing and GVU CenterAtlantaUSA

Personalised recommendations