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Cat Head Detection - How to Effectively Exploit Shape and Texture Features

  • Weiwei Zhang
  • Jian Sun
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)

Abstract

In this paper, we focus on the problem of detecting the head of cat-like animals, adopting cat as a test case. We show that the performance depends crucially on how to effectively utilize the shape and texture features jointly. Specifically, we propose a two step approach for the cat head detection. In the first step, we train two individual detectors on two training sets. One training set is normalized to emphasize the shape features and the other is normalized to underscore the texture features. In the second step, we train a joint shape and texture fusion classifier to make the final decision. We demonstrate that a significant improvement can be obtained by our two step approach. In addition, we also propose a set of novel features based on oriented gradients, which outperforms existing leading features, e. g., Haar, HoG, and EoH. We evaluate our approach on a well labeled cat head data set with 10,000 images and PASCAL 2007 cat data.

Keywords

Texture Feature Object Detection Face Detection Human Detection Texture Detector 
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.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)Google Scholar
  2. 2.
    Everingham, M., van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC 2007) Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
  3. 3.
    Felzenszwalb, P.F.: Learning models for object recognition. In: CVPR, vol. 1, pp. 1056–1062 (2001)Google Scholar
  4. 4.
    Gavrila, D.M., Philomin, V.: Real-time object detection for smart vehicles. In: CVPR, vol. 1, pp. 87–93 (1999)Google Scholar
  5. 5.
    Heisele, B., Serre, T., Pontil, M., Poggio, T.: Component-based face detection. In: CVPR, vol. 1, pp. 657–662 (2001)Google Scholar
  6. 6.
    Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: CVPR, vol. 1, pp. 878–885 (2005)Google Scholar
  7. 7.
    Levi, K., Weiss, Y.: Learning object detection from a small number of examples: the importance of good features. In: CVPR, vol. 2, pp. 53–60 (2004)Google Scholar
  8. 8.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, vol. 2, pp. 1150–1157 (1999)Google Scholar
  9. 9.
    Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Anal. Machine Intell. 23(4), 349–361 (2001)CrossRefGoogle Scholar
  11. 11.
    Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. IEEE Trans. Pattern Anal. Machine Intell. 28(11), 1863–1868 (2006)CrossRefGoogle Scholar
  12. 12.
    Papageorgiou, C., Poggio, T.: A trainable system for object detection. Intl. Journal of Computer Vision 38(1), 15–33 (2000)zbMATHCrossRefGoogle Scholar
  13. 13.
    Ronfard, R., Schmid, C., Triggs, B.: Learning to parse pictures of people. In: ECCV, vol. 4, pp. 700–714 (2004)Google Scholar
  14. 14.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Machine Intell. 20(1), 23–38 (1998)CrossRefGoogle Scholar
  15. 15.
    Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: CVPR (2007)Google Scholar
  16. 16.
    Schneiderman, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: CVPR, vol. 1, pp. 746–751 (2000)Google Scholar
  17. 17.
    Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on riemannian manifolds. In: CVPR (2007)Google Scholar
  18. 18.
    Viola, P., Jones, M.J.: Robust real-time face detection. Intl. Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  19. 19.
    Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: ICCV, vol. 1, pp. 90–97 (2005)Google Scholar
  20. 20.
    Xiao, R., Zhu, H., Sun, H., Tang, X.: Dynamic cascades for face detection. In: ICCV, vol. 1, pp. 1–8 (2007)Google Scholar
  21. 21.
    Zhu, Q., Avidan, S., Yeh, M.-C., Cheng, K.-T.: Fast human detection using a cascade of histograms of oriented gradients. In: CVPR, vol. 2, pp. 1491–1498 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Weiwei Zhang
    • 1
  • Jian Sun
    • 1
  • Xiaoou Tang
    • 2
  1. 1.Microsoft Research AsiaBeijingChina
  2. 2.Dept. of Information EngineeringThe Chinese University of Hong KongHong Kong

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