Advertisement

How Important Are “Deformable Parts” in the Deformable Parts Model?

  • Santosh K. Divvala
  • Alexei A. Efros
  • Martial Hebert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

The Deformable Parts Model (DPM) has recently emerged as a very useful and popular tool for tackling the intra-category diversity problem in object detection. In this paper, we summarize the key insights from our empirical analysis of the important elements constituting this detector. More specifically, we study the relationship between the role of deformable parts and the mixture model components within this detector, and understand their relative importance. First, we find that by increasing the number of components, and switching the initialization step from their aspect-ratio, left-right flipping heuristics to appearance-based clustering, considerable improvement in performance is obtained. But more intriguingly, we observed that with these new components, the part deformations can now be turned off, yet obtaining results that are almost on par with the original DPM detector.

Keywords

Object Detection Good Initialization Part Deformation Deformable Part Camera Viewpoint 
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.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge (2007), http://pascallin.ecs.soton.ac.uk/challenges/VOC
  2. 2.
    Felzenszwalb, P.F., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. PAMI (2010)Google Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR (2005)Google Scholar
  4. 4.
    Jacobs, R., Jordan, M., Nowlan, S., Hinton, G.: Adaptive mixture of local experts. Neural Computation (1991)Google Scholar
  5. 5.
    Xu, L., Neufeld, J., Larson, B., Schuurmans, D.: Maximum margin clustering. In: NIPS (2005)Google Scholar
  6. 6.
    Seemann, E., Leibe, B., Schiele, B.: Multi-aspect detection of articulated objects. In: CVPR (2006)Google Scholar
  7. 7.
    Park, D., Ramanan, D., Fowlkes, C.: Multiresolution Models for Object Detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 241–254. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Yang, W., Toderici, G.: Discriminative tag learning on youtube videos with latent sub-tags. In: CVPR (2011)Google Scholar
  9. 9.
    Felzenszwalb, P.F., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: CVPR (2008)Google Scholar
  10. 10.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D.: Discriminatively trained deformable part models, release 4 (2011), http://people.cs.uchicago.edu/~pff/latent-release4/
  11. 11.
    Felzenszwalb, P.F.: Object detection grammars (2011), http://www.cs.brown.edu/~pff/talks/grammar.pdf
  12. 12.
    Bourdev, L., Maji, S., Brox, T., Malik, J.: Detecting People Using Mutually Consistent Poselet Activations. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 168–181. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Schneiderman, H., Kanade, T.: A statistical method for 3D object detection applied to faces and cars. In: Proc. CVPR, vol. 1, pp. 746–751 (2000)Google Scholar
  14. 14.
    Gu, C., Ren, X.: Discriminative Mixture-of-Templates for Viewpoint Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 408–421. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)Google Scholar
  16. 16.
    Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press (2000)Google Scholar
  17. 17.
    Alexe, B., Petrescu, V., Ferrari, V.: Exploiting spatial overlap to efficiently compute appearance distances between image windows. In: NIPS (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Santosh K. Divvala
    • 1
  • Alexei A. Efros
    • 1
  • Martial Hebert
    • 1
  1. 1.Robotics InstituteCarnegie Mellon UniversityUSA

Personalised recommendations