Sketchable Histograms of Oriented Gradients for Object Detection

  • Ekaterina Zaytseva
  • Santi Seguí
  • Jordi Vitrià
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchable-HoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the stability of the majority orientation and the continuity of gradient orientations. In this way, the sketchable-HoG locally characterizes the complexity of an object model and introduces global structure information while still keeping simplicity, compactness and robustness. We evaluated the proposed image descriptor on publicly Catltech 101 dataset. The obtained results outperforms classical HoG descriptor as well as other reported descriptors in the literature.

Keywords

Object Recognition Feature stability Centrality measures Histogram of Oriented Gradients 

References

  1. 1.
    Berg, A., Berg, T., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 26–33 (2005)Google Scholar
  2. 2.
    Bileschi, S.M., Wolf, L.: Image representations beyond histograms of gradients: The role of gestalt descriptors. In: CVPR. IEEE Computer Society (2007)Google Scholar
  3. 3.
    Brandes, U.: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25, 163–177 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  5. 5.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge (VOC2009) Results (2009), http://www.pascal-network.org/challenges/VOC/voc2009/workshop/index.html
  6. 6.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Workshop on Generative-Model Based Vision (2004)Google Scholar
  7. 7.
    Freeman, L.C.: A Set of Measures of Centrality Based on Betweenness. Sociometry 40(1), 35–41 (1977)CrossRefGoogle Scholar
  8. 8.
    Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Henry Holt and Co., Inc., New York (1982)Google Scholar
  9. 9.
    Serfling, R.J.: Probability Inequalities for the Sum in Sampling without Replacement. The Annals of Statistics 2(1), 39–48 (1974)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc., New York (1995)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ekaterina Zaytseva
    • 1
  • Santi Seguí
    • 1
    • 2
  • Jordi Vitrià
    • 1
    • 2
  1. 1.Computer Vision CenterUniversitat Autònoma de BarcelonaSpain
  2. 2.Dept. de Matemàtica Aplicada i AnàlisiUniversitat de BarcelonaSpain

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