Improved HOG Descriptors in Image Classification with CP Decomposition

  • Tan Vo
  • Dat Tran
  • Wanli Ma
  • Khoa Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)


Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.


HOG tensor CP decomposition Image Classification Support Vector Machine 


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  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  2. 2.
    Khan, N., McCane, B., Wyvill, G.: Sift and surf performance evaluation against various image deformations on benchmark dataset. In: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 501–506 (2011)Google Scholar
  3. 3.
    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, 1627–1645 (2010)CrossRefGoogle Scholar
  4. 4.
    Jiang, J., Xiong, H.: Fast pedestrian detection based on hog-pca and gentle adaboost. In: 2012 International Conference on Computer Science Service System (CSSS), pp. 1819–1822 (2012)Google Scholar
  5. 5.
    Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II–506–II–513 (2004)Google Scholar
  6. 6.
    Carroll, J., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35, 283–319 (1970)CrossRefzbMATHGoogle Scholar
  7. 7.
    Kiers, H.A.: Towards a standardized notation and terminology in multiway analysis. Journal of Chemometrics 14, 105–122 (2000)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kolda, T., Bader, B.: Tensor decompositions and applications. SIAM Review 51, 455–500 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106, 59–70 (2007)CrossRefGoogle Scholar
  10. 10.
    Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tan Vo
    • 1
  • Dat Tran
    • 1
  • Wanli Ma
    • 1
    • 2
  • Khoa Nguyen
    • 1
  1. 1.Faculty of Education, Science, Technology & MathematicsUniversity of CanberraAustralia
  2. 2.Department of Computer ScienceUniversity of Houston DowntownUSA

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