Advertisement

Higher Rank Support Tensor Machines

  • Irene Kotsia
  • Weiwei Guo
  • Ioannis Patras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)

Abstract

This work addresses the two class classification problem within the tensor-based large margin classification paradigm. To this end, we formulate the higher rank Support Tensor Machines (STMs), in which the parameters defining the separating hyperplane form a tensor (tensorplane) that is constrained to be the sum of rank one tensors. The corresponding optimization problem is solved in an iterative manner utilizing the CANDECOMP/PARAFAC (CP) decomposition, where at each iteration the parameters corresponding to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine (SVM)-type optimization problem. The efficiency of the proposed method is illustrated on the problems of gait and action recognition where we report results that improve, in some cases considerably, the state of the art.

Keywords

Recognition Accuracy Action Recognition Machine Intelligence Human Action Recognition Gait Recognition 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Mpca: Multilinear principal component analysis of tensor objects. IEEE Trans. on Neural Networks 19 (2008)Google Scholar
  2. 2.
    Yan, S., Xu, D., Yang, Q., Zhang, L., Tang, X., Zhang, H.J.: Multilinear discriminant analysis for face recognition. IEEE Trans. on Image Processing 16 (2007)Google Scholar
  3. 3.
    Tao, D., Li, X., Wu, X., Hu, W., Maybank, S.J.: Supervised tensor learning. Knowledge and Information Systems 13 (2007)Google Scholar
  4. 4.
    Zafeiriou, S.: Discriminant nonnegative tensor factorization algorithms. IEEE Trans. on Neural Networks 20 (2009)Google Scholar
  5. 5.
    Zafeiriou, S., Petrou, M.: Nonnegative tensor factorization as an alternative csiszar–tusnady procedure: algorithms, convergence, probabilistic interpretations and novel probabilistic tensor latent variable analysis algorithms. Data Mining and Knowledge Discovery 22 (2011)Google Scholar
  6. 6.
    Zafeiriou, S.: Algorithms for nonnegative tensor factorization. Tensors in Image Processing and Computer Vision (2009)Google Scholar
  7. 7.
    Kim, T.-K., Cipolla, R.: Canonical correlation analysis of video volume tensors for action categorization and detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 31 (2009)Google Scholar
  8. 8.
    Bobick, A., Davis, J.: The recognition of human movements using temporal templates. IEEE Trans. on Pattern Analysis and Machine Intelligence 23 (2001)Google Scholar
  9. 9.
    Wolf, L., Jhuang, H., Hazan, T.: Modeling appearances with low-rank svm. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  10. 10.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 29 (2007)Google Scholar
  11. 11.
    Loy, C.C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  12. 12.
    Boulgouris, N.V., Plataniotis, K.N., Hatzinakos, D.: Gait recognition using linear time normalization. Pattern Recognition 39 (2006)Google Scholar
  13. 13.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The humanid gait challenge problem: Data sets, performance, and analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 27 (2005)Google Scholar
  14. 14.
    Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. on Pattern Analysis and Machine Intelligence 28 (2006)Google Scholar
  15. 15.
    Tao, D., Li, X., Wu, X., Maybank, S.J.: General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 29 (2007)Google Scholar
  16. 16.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51 (2009)Google Scholar
  17. 17.
    Kale, A., Rajagopalan, A.N., Sunderesan, A., N. Cuntoor, A.R.C., Krueger, V., Chellappa, R.: Identification of humans using gait. IEEE Trans. on Image Processing (2004)Google Scholar
  18. 18.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach (2004)Google Scholar
  19. 19.
    Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: Subspace learning from image gradient orientations. IEEE Trans. on Pattern Analysis and Machine Intelligence (2012)Google Scholar
  20. 20.
    Oikonomopoulos, A., Patras, I., Pantic, M.: Spatiotemporal localization and categorization of human actions in unsegmented image sequences. IEEE Trans. on Image Processing 20 (2011)Google Scholar
  21. 21.
    Ahmad, M., Lee, S.: Human action recognition using shape and clg-motion flow from multi-view image sequences. Pattern Recognition 41 (2008)Google Scholar
  22. 22.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (2005)Google Scholar
  23. 23.
    Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79 (2008)Google Scholar
  24. 24.
    Fathi, A., Mori, G.: Action recognition by learning mid-level motion features. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 41 (2008)Google Scholar
  25. 25.
    Jhuang, H., Serre, T., Wolf, L., Poggio, T.: A biologically inspired system for action recognition. In: Proceedings of IEEE Conference on Computer Vision (2007)Google Scholar
  26. 26.
    Rapantzikos, K., Avrithis, Y., Kollias, S.: Dense saliency-based spatiotemporal feature points for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  27. 27.
    Ali, S., Shah, M.: Human action recognition in videos using kinematic features and multiple instance learning. IEEE Trans. on Pattern Analysis and Machine Intelligence 1 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Irene Kotsia
    • 1
  • Weiwei Guo
    • 2
  • Ioannis Patras
    • 1
  1. 1.School of Electronic Engineering and Computer ScienceQueen Mary, University of LondonUK
  2. 2.College of Electronic Science and EngineeringNational University of Defense TechnologyChina

Personalised recommendations