Beyond HOG: Learning Local Parts for Object Detection

  • Chenjie HuangEmail author
  • Zheng Qin
  • Kaiping Xu
  • Guolong Wang
  • Tao Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9314)


Histogram of Oriented Gradients (HOG) features have laid solid foundation for object detection in recent years for its both accuracy and speed. However, the expressivity of HOG is limited because the simple gradient features may ignore some important local information about objects and HOG is actually data-independent. In this paper, we propose to replace HOG by a parts-based representation, Histogram of Local Parts (HLP), for object detection under sliding window framework. HLP can capture richer and larger local patterns of objects and are more expressive than HOG. Specifically, we adopt Sparse Nonnegative Matrix Factorization to learn an over-complete parts-based dictionary from data. Then we can obtain HLP representation for a local patch by aggregating the Local Parts coefficients of pixels in this patch. Like DPM, we can train a supervised model with HLP given the latent positions of roots and parts of objects. Extensive experiments on INRIA and PASCAL datasets verify the superiority of HLP to state-of-the-art HOG-based methods for object detection, which shows that HLP is more effective than HOG.


Object detection Feature learning 


  1. 1.
    Bourdev, L., Malik, J.: Poselets: body part detectors trained using 3d human pose annotations. In: ICCV (2009)Google Scholar
  2. 2.
    Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. TPAMI 33(8), 1548–1560 (2011)CrossRefGoogle Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  4. 4.
    Dikmen, M., Hoiem, D., Huang, T.S.: A data-driven method for feature transformation. In: CVPR (2012)Google Scholar
  5. 5.
    Ding, G., Guo, Y., Zhou, J.: Collective matrix factorization hashing for multimodal data. In: CVPR (2014)Google Scholar
  6. 6.
    Divvala, S., Efros, A., Hebert, M.: How important are deformable parts in the deformable parts model? In: ECCV (2012)Google Scholar
  7. 7.
    Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC (2009)Google Scholar
  8. 8.
    Everingham, M., Gool, L.V., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88(2), 303–338 (2010)CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. TPAMI 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  10. 10.
    Girshick, R., Felzenszwalb, P., McAllester, D.: Object detection with grammar models. In: NIPS (2011)Google Scholar
  11. 11.
    Guo, Y., Ding, G., Jin, X., Wang, J.: Learning predictable and discriminative attributes for visual recognition. In: AAAI (2015)Google Scholar
  12. 12.
    Hoyer, P.O.: Non-negative matrix factorization with sparseness constraints. JMLR 5, 1457–1469 (2004)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Hussain, S., Kuntzmann, L., Triggs, B.: Feature sets and dimensionality reduction for visual object detection. In: BMVC (2010)Google Scholar
  14. 14.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by nonnegative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  15. 15.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)Google Scholar
  16. 16.
    Malisiewicz, T., Gupta, A., Efros, A.: Ensemble of exemplar-svms for object detection and beyond. In: ICCV (2011)Google Scholar
  17. 17.
    Pirsiavash, H., Ramanan, D., Fowlkes, C.: Bilinear classifiers for visual recognition. In: NIPS (2009)Google Scholar
  18. 18.
    Ren, X., Ramanan, D.: Histograms of sparse codes for object detection. In: CVPR (2013)Google Scholar
  19. 19.
    Roshtkhari, M.J., Levine, M.D.: Online dominant and anomalous behavior detection in videos. In: CVPR (2013)Google Scholar
  20. 20.
    Schwartz, W., Kembhavi, A., Harwood, D., Davis, L.: Human detection using partial least squares analysis. In: ICCV (2009)Google Scholar
  21. 21.
    Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: ICCV (2009)Google Scholar
  22. 22.
    Vijayanarasimhan, S., Grauman, K.: Efficient region search for object detection. In: CVPR (2011)Google Scholar
  23. 23.
    Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)Google Scholar
  24. 24.
    Wachsmuth, M.W.O.E., Perrett, D.I.: Recognition of objects and their component parts: responses of single units in the temporal cortex of the macaque. Cereb. Cortex 4(5), 509–522 (1994)CrossRefGoogle Scholar
  25. 25.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T.S., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR 2010 (2010)Google Scholar
  26. 26.
    Xiao, J., Hays, J., Ehinger, K., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: CVPR (2010)Google Scholar
  27. 27.
    Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR (2011)Google Scholar
  28. 28.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Chenjie Huang
    • 1
    Email author
  • Zheng Qin
    • 1
  • Kaiping Xu
    • 1
  • Guolong Wang
    • 1
  • Tao Xu
    • 1
  1. 1.School of Software, TNListTsinghua UniversityBeijingChina

Personalised recommendations