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LACBoost and FisherBoost: Optimally Building Cascade Classifiers

  • Chunhua Shen
  • Peng Wang
  • Hanxi Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall classification error. Although there are a few reported methods addressing this requirement in the context of object detection, there is no a principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such a boosting algorithm in this work. It is inspired by the linear asymmetric classifier (LAC) of [1] in that our boosting algorithm optimizes a similar cost function. The new totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on face detection suggest that our proposed boosting algorithms can improve the state-of-the-art methods in detection performance.

Keywords

Linear Discriminant Analysis Quadratic Programming Object Detection Column Generation 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.

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References

  1. 1.
    Wu, J., Mullin, M.D., Rehg, J.M.: Linear asymmetric classifier for cascade detectors. In: Proc. Int. Conf. Mach. Learn., Bonn, Germany, pp. 988–995 (2005)Google Scholar
  2. 2.
    Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comp. Vis. 57(2), 137–154 (2004)CrossRefGoogle Scholar
  3. 3.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric AdaBoost and a detector cascade. In: Proc. Adv. Neural Inf. Process. Syst., pp. 1311–1318. MIT Press, Cambridge (2002)Google Scholar
  4. 4.
    Wu, J., Brubaker, S.C., Mullin, M.D., Rehg, J.M.: Fast asymmetric learning for cascade face detection. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 369–382 (2008)CrossRefGoogle Scholar
  5. 5.
    Demiriz, A., Bennett, K., Shawe-Taylor, J.: Linear programming boosting via column generation. Mach. Learn. 46(1-3), 225–254 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Bourdev, L., Brandt, J.: Robust object detection via soft cascade. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., San Diego, CA, US, pp. 236–243 (2005)Google Scholar
  7. 7.
    Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascades for face detection. In: Proc. IEEE Int. Conf. Comp. Vis., Rio de Janeiro, Brazil (2007)Google Scholar
  8. 8.
    Pham, M.T., Hoang, V.D.D., Cham, T.J.: Detection with multi-exit asymmetric boosting. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Anchorage, Alaska (2008)Google Scholar
  9. 9.
    Li, S.Z., Zhang, Z.: FloatBoost learning and statistical face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1112–1123 (2004)CrossRefGoogle Scholar
  10. 10.
    Liu, C., Shum, H.Y.: Kullback-Leibler boosting. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Madison, Wisconsin, vol. 1, pp. 587–594 (June 2003)Google Scholar
  11. 11.
    Rätsch, G., Mika, S., Schölkopf, B., Müller, K.R.: Constructing boosting algorithms from SVMs: An application to one-class classification. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1184–1199 (2002)CrossRefGoogle Scholar
  12. 12.
    Beck, A., Teboulle, M.: Mirror descent and nonlinear projected subgradient methods for convex optimization. Oper. Res. Lett. 31(3), 167–175 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Collins, M., Globerson, A., Koo, T., Carreras, X., Bartlett, P.L.: Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks. J. Mach. Learn. Res., 1775–1822 (2008)Google Scholar
  14. 14.
    MOSEK ApS: The MOSEK optimization toolbox for matlab manual, version 5.0, revision 93 (2008), http://www.mosek.com/
  15. 15.
    Paisitkriangkrai, S., Shen, C., Zhang, J.: Efficiently training a better visual detector with sparse Eigenvectors. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Miami, Florida, US (June 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chunhua Shen
    • 1
    • 2
  • Peng Wang
    • 3
  • Hanxi Li
    • 2
    • 1
  1. 1.Canberra Research LaboratoryNICTAAustralia
  2. 2.Australian National UniversityAustralia
  3. 3.Beihang UniversityBeijingChina

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