A New Multiple Kernel Approach for Visual Concept Learning

  • Jingjing Yang
  • Yuanning Li
  • Yonghong Tian
  • Lingyu Duan
  • Wen Gao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)


In this paper, we present a novel multiple kernel method to learn the optimal classification function for visual concept. Although many carefully designed kernels have been proposed in the literature to measure the visual similarity, few works have been done on how these kernels really affect the learning performance. We propose a Per-Sample Based Multiple Kernel Learning method (PS-MKL) to investigate the discriminative power of each training sample in different basic kernel spaces. The optimal, sample-specific kernel is learned as a linear combination of a set of basic kernels, which leads to a convex optimization problem with a unique global optimum. As illustrated in the experiments on the Caltech 101 and the Wikipedia MM dataset, the proposed PS-MKL outperforms the traditional Multiple Kernel Learning methods (MKL) and achieves comparable results with the state-of-the-art methods of learning visual concepts.


Visual Concept Learning Support Vector Machine Multiple Kernel Learning 


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  1. 1.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. PAMI 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  2. 2.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: ICCV Workshop on Content-based Access of Image and Video Databases, Bombay, India, pp. 42–50 (1998)Google Scholar
  3. 3.
    Vogel, J., Schiele, B.: Natural Scene Retrieval Based on a Semantic Modeling Step. In: Proc. Int’l. Conf. Image and Video Retrieval (July 2004)Google Scholar
  4. 4.
    Sivic, J., Russell, B., Efros, A., Zisserman, A.: Discovering Objects and Their Location in Images. In: Proceedings of the IEEE ICCV 2005, pp. 370–377 (2005)Google Scholar
  5. 5.
    Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning Object Categories from Google’s Image Search. In: Proceedings of the Tenth ICCV 2005, vol. 2, pp. 1816–1823 (2005)Google Scholar
  6. 6.
  7. 7.
    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: Conference on Computer Vision and Pattern Recognition Workshop (2004) Google Scholar
  8. 8.
    Kumar, A., Sminc, C.: Support Kernel Machines for Object Recognition. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007, October 14-21, 2007, pp. 1–8 (2007)Google Scholar
  9. 9.
    Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: Proc. Computer Vision and Pattern Recognition (2005)Google Scholar
  10. 10.
    Fei-Fei, L., Fergus, R., Perona, P.: One-Shot learning of object categories. IEEE Trans. PAMI 28(4), 594–611 (2006)CrossRefGoogle Scholar
  11. 11.
    Jia, L., Fei-Fei, L.: What, where and who? Classifying event by scene and object recognition. In: ICCV (2007)Google Scholar
  12. 12.
    Ng, A., Jordan, M.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In: Advances in NIPS, vol. 12 (2002)Google Scholar
  13. 13.
    Malisiewicz, T., Efros, A.A.: Recognition by Association via Learning Per-exemplar Distances. In: CVPR (June 2008)Google Scholar
  14. 14.
    Torralba, A., Fergus, R., Freeman, W.T.: Tiny images.Technical Report MIT-CSAIL-TR-2007-024, MIT CSAIL (2007)Google Scholar
  15. 15.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  16. 16.
    Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: ICCV, October 17-21, 2005, vol. 2, pp. 1458–1465 (2005)Google Scholar
  17. 17.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)Google Scholar
  18. 18.
    Ling, H., Soatto, S.: Proximity Distribution Kernels for Geometric Context in Category Recognition. In: ICCV, October 14-21, 2007, pp. 1–8 (2007)Google Scholar
  19. 19.
    Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple kernel learning, conic duality, and the SMO algorithm. In: NIPS (2004)Google Scholar
  20. 20.
    Sonnenburg, S., Raetsch, G., Schaefer, C., Scholkopf, B.: Large scale multiple kernel learning. Journal of Machine Learning Research, 1531–1565 (2006)Google Scholar
  21. 21.
    Frome, A., Singer, Y., Sha, F., Malik, J.: Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. In: ICCV 2007, pp. 1–8 (2007)Google Scholar
  22. 22.
    Zhang, H., Berg, A.C., Maire, M., Malik, J.: SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition. In: CVPR. pp. 2126–2136 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jingjing Yang
    • 1
    • 2
    • 3
  • Yuanning Li
    • 1
    • 2
    • 3
  • Yonghong Tian
    • 3
  • Lingyu Duan
    • 3
  • Wen Gao
    • 1
    • 2
    • 3
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate SchoolChinese Academy of SciencesBeijingChina
  3. 3.The Institute of Digital Media, School of EE & CSPeking UniversityBeijingChina

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