Discriminative Prior Bias Learning for Pattern Classification

Takumi Kobayashi, Kenji Nishida

Abstract

Prior information has been effectively exploited mainly using probabilistic models. In this paper, by focusing on the bias embedded in the classifier, we propose a novel method to discriminatively learn the prior bias based on the extra prior information assigned to the samples other than the class category, e.g., the 2-D position where the local image feature is extracted. The proposed method is formulated in the framework of maximum margin to adaptively optimize the biases, improving the classification performance. We also present the computationally efficient optimization approach that makes the method even faster than the standard SVM of the same size. The experimental results on patch labeling in the on-board camera images demonstrate the favorable performance of the proposed method in terms of both classification accuracy and computation time.

References

  1. Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press, New York, NY.
  2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer, Berlin, Germany.
  3. Brostow, G. J., Shotton, J., Fauqueur, J., and Cipolla, R. (2008). Segmentation and recognition using structure from motion point clouds. In ECCV'08, the 10th European Conference on Computer Vision, pages 44-57.
  4. Chang, C.-C. and Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm.
  5. Cremers, D. and Grady, L. (2006). Statistical priors for efficient combinatorial optimization via graph cuts. In ECCV'06, the 9th European Conference on Computer Vision, pages 263-274.
  6. El-Baz, A. and Gimel'farb, G. (2009). Robust image segmentation using learned priors. In ICCV'09, the 12nd International Conference on Computer Vision, pages 857-864.
  7. Fan, R.-E., Chen, P.-H., and Lin, C.-J. (2005). Working set selection using second order information for training support vector machines. Journal of Machine Learning Research, 6:1889-1918.
  8. Gao, T., Stark, M., and Koller, D. (2012). What makes a good detector? - structured priors for learning from few examples. In ECCV'12, the 12th International Conference on Computer Vision, pages 354-367.
  9. Ghosh, J. and Ramamoorthi, R. (2003). Bayesian Nonparametrics. Springer, Berlin, Germany.
  10. Jiang, T., Jurie, F., and Schmid, C. (2009). Learning shape prior models for object matching. In CVPR'09, the 22nd IEEE Conference on Computer Vision and Pattern Recognition, pages 848-855.
  11. Jie, L., Tommasi, T., and Caputo, B. (2011). Multiclass transfer learning from unconstrained priors. In ICCV'11, the 13th International Conference on Computer Vision, pages 1863-1870.
  12. Joachims, T. (1999). Making large-scale svm learning practical. In Schölkopf, B., Burges, C., and Smola, A., editors, Advances in Kernel Methods - Support Vector Learning, pages 169-184. MIT Press, Cambridge, MA, USA.
  13. Kan, M., Shan, S., Zhang, H., Lao, S., and Chen, X. (2012). Multi-view discriminant analysis. In ECCV'12, the 12th International Conference on Computer Vision, pages 808-821.
  14. Kapoor, A., Hua, G., Akbarzadeh, A., and Baker, S. (2009). Which faces to tag: Adding prior constraints into active learning. In ICCV'09, the 12nd International Conference on Computer Vision, pages 1058-1065.
  15. Kobayashi, T. and Otsu, N. (2008). Image feature extraction using gradient local auto-correlations. In ECCV'08, the 10th European Conference on Computer Vision, pages 346-358.
  16. Platt, J. (1999). Fast training of support vector machines using sequential minimal optimization. In Schölkopf, B., Burges, C., and Smola, A., editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208. MIT Press, Cambridge, MA, USA.
  17. Poggio, T., Mukherjee, S., Rifkin, R., Rakhlin, A., and Verri, A. (2001). b. Technical Report CBCL Paper #198/AI Memo #2001-011, Massachusetts Institute of Technology, Cambridge, MA, USA.
  18. Sharma, A. and Jacobs, D. (2011). Bypassing synthesis: Pls for face recognition with pose, low-resolution and sketch. In CVPR'11, the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 593-600.
  19. Smola, A. J., Bartlett, P., Schölkopf, B., and Schuurmans, D. (2000). Advances in Large-Margin Classifiers. MIT Press, Cambridge, MA, USA.
  20. Van Gestel, T., Suykens, J., Lanckriet, G., Lambrechts, A., De Moor, B., and Vandewalle, J. (2002). Bayesian framework for least squares support vector machine classifiers, gaussian processes and kernel fisher discriminant analysis. Neural Computation, 15(5):1115- 1148.
  21. Vapnik, V. (1998). Statistical Learning Theory. Wiley, New York, NY, USA.
  22. Wang, C., Liao, X., Carin, L., and Dunson, D. (2010). Classification with incomplete data using dirichlet process priors. The Journal of Machine Learning Research, 11:3269-3311.
  23. Yuan, C., Hu, W., Tian, G., Yang, S., and Wang, H. (2013). Multi-task sparse learning with beta process prior for action recognition. In CVPR'13, the 26th IEEE Conference on Computer Vision and Pattern Recognition, pages 423-430.
Download


Paper Citation


in Harvard Style

Kobayashi T. and Nishida K. (2014). Discriminative Prior Bias Learning for Pattern Classification . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 67-75. DOI: 10.5220/0004813600670075


in Bibtex Style

@conference{icpram14,
author={Takumi Kobayashi and Kenji Nishida},
title={Discriminative Prior Bias Learning for Pattern Classification},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={67-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004813600670075},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Discriminative Prior Bias Learning for Pattern Classification
SN - 978-989-758-018-5
AU - Kobayashi T.
AU - Nishida K.
PY - 2014
SP - 67
EP - 75
DO - 10.5220/0004813600670075