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Image Classification Using Super-Vector Coding of Local Image Descriptors

  • Xi Zhou
  • Kai Yu
  • Tong Zhang
  • Thomas S. Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

This paper introduces a new framework for image classification using local visual descriptors. The pipeline first performs a nonlinear feature transformation on descriptors, then aggregates the results together to form image-level representations, and finally applies a classification model. For all the three steps we suggest novel solutions which make our approach appealing in theory, more scalable in computation, and transparent in classification. Our experiments demonstrate that the proposed classification method achieves state-of-the-art accuracy on the well-known PASCAL benchmarks.

Keywords

Vector Quantization Local Descriptor Kullback Leibler Codebook Size Spatial Pyramid Match 
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.

Supplementary material

978-3-642-15555-0_11_MOESM1_ESM.pdf (3.4 mb)
Electronic Supplementary Material (3,507 KB)

References

  1. 1.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, p. 22 (2004) (Citeseer)Google Scholar
  2. 2.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories (2005) (Citeseer)Google Scholar
  3. 3.
    Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering object categories in image collections. In: Proc. ICCV, vol. 2 (2005)Google Scholar
  4. 4.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories (2006) (Citeseer)Google Scholar
  5. 5.
    MarcAurelio Ranzato, F., Boureau, Y., LeCun, Y.: Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: Proc. Computer Vision and Pattern Recognition Conference (CVPR 2007) (2007) (Citeseer)Google Scholar
  6. 6.
    Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 994 (2005) (Citeseer)Google Scholar
  7. 7.
    Zhang, H., Berg, A., Maire, M., Malik, J.: SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In: Proc. CVPR, vol. 2, pp. 2126–2136 (2006) (Citeseer)Google Scholar
  8. 8.
    Makadia, A., Pavlovic, V., Kumar, S.: A new baseline for image annotation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 316–329. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)Google Scholar
  10. 10.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM international conference on Image and video retrieval, p. 408. ACM, New York (2007)Google Scholar
  11. 11.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 106, 59–70 (2007)CrossRefGoogle Scholar
  12. 12.
    Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) Challenge. International Journal of Computer Vision (2009)Google Scholar
  13. 13.
    Varma, M., Ray, D.: Learning the discriminative power-invariance trade-off. In: Proc. ICCV, vol. 2007 (2007) (Citeseer)Google Scholar
  14. 14.
    Marszalek, M., Schmid, C., Harzallah, H., Weijer, J.V.D.: Learning object representations for visual object class recognition. In: Visual Recognition Challange workshop, in conjunction with ICCV (2007)Google Scholar
  15. 15.
    Jebara, T., Kondor, R.: Bhattacharyya and expected likelihood kernels. In: Proceedings of Learning theory and Kernel machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, p. 57. Springer, Heidelberg (2003)Google Scholar
  16. 16.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 2278–2324 (1998)CrossRefGoogle Scholar
  17. 17.
    Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.: Self-taught learning: Transfer learning from unlabeled data. In: Proceedings of the 24th international conference on Machine learning, p. 766. ACM, New York (2007)Google Scholar
  18. 18.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  19. 19.
    Yu, K., Zhang, T., Gong, Y.: Nonlinear Learning using Local Coordinate Coding. In: NIPS (2009)Google Scholar
  20. 20.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning. Adv. NIPS 21 (2009)Google Scholar
  21. 21.
    Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: Proc. CVPR (2006) (Citeseer)Google Scholar
  22. 22.
    Zhou, X., Cui, N., Li, Z., Liang, F., Huang, T.: Hierarchical Gaussianization for Image Classification. In: ICCV (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xi Zhou
    • 1
  • Kai Yu
    • 2
  • Tong Zhang
    • 3
  • Thomas S. Huang
    • 1
  1. 1.Dept. of ECEUniversity of Illnois at Urbana-Champaign 
  2. 2.NEC Laboratories AmericaCupertino
  3. 3.Department of StatisticsRutgers University 

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