PCA Enhanced Training Data for Adaboost

  • Arne Ehlers
  • Florian Baumann
  • Ralf Spindler
  • Birgit Glasmacher
  • Bodo Rosenhahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6854)

Abstract

In this paper we propose to enhance the training data of boosting-based object detection frameworks by the use of principal component analysis (PCA). The quality of boosted classifiers highly depends on the image databases exploited in training. We observed that negative training images projected into the objects PCA space are often far away from the object class. This broad boundary between the object classes in training can yield to a high classification error of the boosted classifier in the testing phase. We show that transforming the negative training database close to the positive object class can increase the detection performance. In experiments on face detection and the analysis of microscopic cell images, our method decreases the amount of false positives while maintaining a high detection rate. We implemented our approach in a Viola & Jones object detection framework using AdaBoost to combine Haar-like features. But as a preprocessing step our method can easily be integrated in all boosting-based frameworks without additional overhead.

Keywords

Principal Component Analysis Object Space Object Class Face Detection Independent Component Analysis 
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.
    Ali, S., Shah, M.: An integrated approach for generic object detection using kernel pca and boosting. In: ICME, pp. 1030–1033 (2005)Google Scholar
  2. 2.
    Bartlett, M., Movellan, J., Sejnowski, T.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  3. 3.
    Baumann, F., Ernst, K., Ehlers, A., Rosenhahn, B.: Symmetry enhanced adaboost. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 286–295. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: SIGGRAPH 1999: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA (1999)Google Scholar
  5. 5.
    Crowther, P.S., Cox, R.J.: A method for optimal division of data sets for use in neural networks. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 1–7. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Freund, Y., Schapire, R.E.: A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)Google Scholar
  7. 7.
    Homepage, F.D.: (2010), http://www.facedetection.com/
  8. 8.
    Leistner, C., Grabner, H., Bischof, H.: Semi-supervised boosting using visual similarity learning. In: CVPR (2008)Google Scholar
  9. 9.
    Li, H., Shen, C.: Boosting the minimum margin: Lpboost vs. adaboost. In: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, DICTA, pp. 533–539 (2008)Google Scholar
  10. 10.
    Schapire, R.E., Freund, Y., Barlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. In: Proceedings of the Fourteenth International Conference on Machine Learning (ICML), pp. 322–330 (1997)Google Scholar
  11. 11.
    Schölkopf, B., Mika, S., Smola, A., Rätsch, G., Müller, K.R.: Kernel pca pattern reconstruction via approximate pre-images. In: Proceedings of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing, pp. 147–152. Springer, Heidelberg (1998)Google Scholar
  12. 12.
    Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591. IEEE Computer Society, Los Alamitos (1991)Google Scholar
  13. 13.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  14. 14.
    Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. Advances in Neural Information Processing 18, 1417–1426 (2007)Google Scholar
  15. 15.
    Warmuth, M.K., Glocer, K., Raetsch, G.: Boosting algorithms for maximizing the soft margin. Advances in Neural Information Processing Systems 20, 1585–1592 (2008)Google Scholar
  16. 16.
    Warmuth, M.K., Glocer, K.A., Vishwanathan, S.: Entropy regularized lpboost. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds.) ALT 2008. LNCS (LNAI), vol. 5254, pp. 256–271. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 34–58 (2002)CrossRefGoogle Scholar
  18. 18.
    Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Microsoft Research Technical Report, MSR-TR-2010-66 (2010)Google Scholar
  19. 19.
    Zhang, D., Li, S.Z., Gatica-Perez, D.: Real-time face detection using boosting in hierarchical feature spaces. In: ICPR, vol. (2), pp. 411–414 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Arne Ehlers
    • 1
  • Florian Baumann
    • 1
  • Ralf Spindler
    • 2
  • Birgit Glasmacher
    • 2
  • Bodo Rosenhahn
    • 1
  1. 1.Institut für InformationsverarbeitungLeibniz Universitüt HannoverGermany
  2. 2.Institut für MehrphasenprozesseLeibniz Universität HannoverGermany

Personalised recommendations