Efficient Boosted Weak Classifiers for Object Detection

  • Xiaopeng Hong
  • Guoying Zhao
  • Haoyu Ren
  • Xilin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)


This paper accelerates boosted nonlinear weak classifiers in boosting framework for object detection. Although conventional nonlinear classifiers are usually more powerful than linear ones, few existing methods integrate them into boosting framework as weak classifiers owing to the highly computational cost. To address this problem, this paper proposes a novel nonlinear weak classifier named Partition Vector weak Classifier (PVC), which is based on the histogram intersection kernel functions of the feature vector with respect to a set of pre-defined Partition Vectors (PVs). A three-step algorithm is derived from the kernel trick for efficient weak learning. The obtained PVCs are further accelerated via building a look-up table. Experimental results in the detection tasks for multiple classes of objects show that boosted PVCs significantly improves both learning and evaluation efficiency of nonlinear SVMs to the level of boosted linear classifiers, without losing any of the high discriminative power.


Support Vector Machine Object Detection Linear Classifier Weak Classifier Linear Support Vector Machine 
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.


  1. 1.
    Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian Detection: An Evaluation of the State of the Art. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(4), 743–761 (2012)CrossRefGoogle Scholar
  2. 2.
    Enzweiler, M., Gavrila, D.: Monocular pedestrian detection: Survey and experiments. IEEE Transactions on Patten Analysis and Machine Intelligence 31(12), 2179–2195 (2009)CrossRefGoogle Scholar
  3. 3.
    Gall, J., Yao, A., Razavi, N., Van, G.L., Lempitsky, V.: Hough Forests for Object Detection, Tracking, and Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(11), 2188–2202 (2011)CrossRefGoogle Scholar
  4. 4.
    Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  5. 5.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR (2005)Google Scholar
  6. 6.
    Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple Kernels for Object Detection. In: ICCV (2009)Google Scholar
  7. 7.
    Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: CVPR (2001)Google Scholar
  8. 8.
    Zhu, Q., Avidan, S., Yeh, M., Cheng, K.: Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. In: CVPR (2006)Google Scholar
  9. 9.
    Laptev, I.: Improvements of Object Detection Using Boosted Histograms. In: BMVC (2006)Google Scholar
  10. 10.
    Dollár, P., Tu, Z., Tao, H., Belongie, S.: Feature Mining for Image Classification. In: CVPR (2007)Google Scholar
  11. 11.
    Tuzel, O., Porikli, F., Meer, P.: Pedestrian Detection via Classification on Riemannian Manifolds. In: PAMI, vol. 30, pp. 1713–1727 (2008)Google Scholar
  12. 12.
    Wu, B., Nevatia, R.: Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection. In: ICCV (2007)Google Scholar
  13. 13.
    Wu, B., Nevatia, R.: Optimizing Discrimination- Efficiency Tradeoff in Integrating Heterogeneous Local Features for Object Detection. In: CVPR (2008)Google Scholar
  14. 14.
    Zhang, J., Huang, K., Yu, Y., Tan, T.: Boosted local structured HOG-LBP for object localization. In: CVPR (2011)Google Scholar
  15. 15.
    Wang, X., Han, X., Yan, S.: An HOG-LBP Human Detector with Partial Occlusion Handling. In: ICCV (2009)Google Scholar
  16. 16.
    Maji, S., Berg, A., Malik, J.: Classification Using Intersection Kernel Support Vector Machines is Efficient. In: CVPR (2008)Google Scholar
  17. 17.
    Maji, S., Berg, A.: Max-Margin Additive Classifiers for Detection. In: ICCV (2009)Google Scholar
  18. 18.
    Wojek, C., Schiele, B.: A Performance Evaluation of Single and Multi-feature People Detection. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 82–91. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Wojek, C., Walk, S., Schiele, B.: Multi-Cue Onboard Pedestrian Detection. In: CVPR (2009)Google Scholar
  20. 20.
    Yang, M.-H., Roth, D., Ahuja, N.: A Tale of Two Classifiers: Snow vs. SVM in Visual Recognition. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 685–699. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Schapire, R., Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Predictions. In: Machine Learning, vol. 37, pp. 297–336 (1999)Google Scholar
  22. 22.
    Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: LIBLINEAR: A library for large linear classification Journal of Machine Learning Research 9, 1871–1874 (2008)Google Scholar
  23. 23.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiaopeng Hong
    • 1
  • Guoying Zhao
    • 1
  • Haoyu Ren
    • 2
  • Xilin Chen
    • 2
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of OuluFinland
  2. 2.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing TechnologyCASP.R. China

Personalised recommendations