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Activity Recognition in Still Images with Transductive Non-negative Matrix Factorization

  • Naiyang GuanEmail author
  • Dacheng Tao
  • Long Lan
  • Zhigang Luo
  • Xuejun Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)

Abstract

Still image based activity recognition is a challenging problem due to changes in appearance of persons, articulation in poses, cluttered backgrounds, and absence of temporal features. In this paper, we proposed a novel method to recognize activities from still images based on transductive non-negative matrix factorization (TNMF). TNMF clusters the visual descriptors of each human action in the training images into fixed number of groups meanwhile learns to represent the visual descriptor of test image on the concatenated bases. Since TNMF learns these bases on both training images and test image simultaneously, it learns a more discriminative representation than standard NMF based methods. We developed a multiplicative update rule to solve TNMF and proved its convergence. Experimental results on both laboratory and real-world datasets demonstrate that TNMF consistently outperforms NMF.

Keywords

Still image based action recognition Non-negative matrix factorization Transductive learning 

References

  1. 1.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: International Conference on Computer Vision, vol. 2, pp. 1395–1402 (2005)Google Scholar
  2. 2.
    Laptev, I., Perez, P.: Retrieving actions in movies. In: Proceedings of International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  3. 3.
    Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. International Journal of Computer Vision 79(3), 299–318 (2008)CrossRefGoogle Scholar
  4. 4.
    Thurau, C., Hlavac, V.: Pose primitive based human action recognition in videos or still images. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  5. 5.
    Aggarwal, J.K., Xia, L.: Human Activity Recognition from 3DData: A Review. Pattern Recognition Letters (2014)Google Scholar
  6. 6.
    Waltner, G., Mauthner, T., Bischof, H.: Indoor Activity Detection and Recognition for Sport Games Analysis. arXiv preprint arXiv:1404.6413 (2014)
  7. 7.
    Lee, D.D., Seung, H.S.: Learning the Parts of Objects with Non-negative Matrix Factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  8. 8.
    Xu, W., Liu, X., Gong, Y.: Document clustering based on nonnegative matrix factorization. In: ACM Special Interest Group on Information Retrieval, pp. 167–273 (2014)Google Scholar
  9. 9.
    Huang, X., Zheng, X., Yuan, W., Wang, F., Zhu, S.: Enhanced Clustering of Biomedical Documents Using Ensemble Nonnegative Matrix Factorization. Information Sciences 181(11), 2293–2302 (2011)CrossRefGoogle Scholar
  10. 10.
    Pauca, V., Piper, J., Plemmons, R.: Nonnegative Matrix Factorization for Spectral Data Analysis. Linear Algebra and its Applications 416(1), 29–47 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Liu, L., Shao, L., Zhen, X., Li, X.: Learning Discriminative Key Poses for Action Recognition. IEEE Transactions on Cybernetics 43(6), 1860–1870 (2013)CrossRefGoogle Scholar
  12. 12.
    Zhang, Z., Tao, D.: Slow Feature Analysis for Human Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 436–450 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Liu, L., Shao, L., Zheng, F., Li, X.: Realistic Action Recognition via Sparsely-constructed Gaussian Processes. Pattern Recognition 47, 3819–3827 (2014)CrossRefGoogle Scholar
  14. 14.
    Hotelling, H.: Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology 24, 417–441 (1933)CrossRefzbMATHGoogle Scholar
  15. 15.
    Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
  16. 16.
    Guan, N., Lan, L., Tao, D., Luo, Z., Yang, X.: Transductive nonnegative matrix factorizationfor semi-supervised high-performance speech separation. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 2553–2557 (2014)Google Scholar
  17. 17.
    Lee, D.D., Seung, H.S.: Algorithms for Non-negative matrix factorization. In: Proceedings of Advances in Neural Information and Processing Systems, pp. 556–562 (2000)Google Scholar
  18. 18.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 7, pp. 1–8 (2008)Google Scholar
  19. 19.
    Ikizler-Cinbis, N., Cinbis, R.G., Sclaroff, S.: Learning actions from the web. In: IEEE International Conference on Computer Vision, pp. 995–1002 (2009)Google Scholar
  20. 20.
    Zheng, Y., Zhang, Y.J., Li, X., Liu, B.D.: Action recognition in still images using a combination of human pose and context information. In: International Conference on Image Processing (2012)Google Scholar
  21. 21.
    Khan, F.S., Anwer, R.M., van deWeijer, J., Bagdanov, A.D., Lopez, A.M., Felsberg, M.: Coloring Action Recognition in Still Images. International Journal of Computer Vision 105, 205–221 (2013)CrossRefGoogle Scholar
  22. 22.
    Delaitre, V., Laptev, I., Sivic, J.: Recognizing human actions in still images: astudy of bag-of-features and part-based representations. In: British Machine Vision Conference (2010)Google Scholar
  23. 23.
    Laptev, I.: On Space-time Interest Points. International Journal of Computer Vision 64, 107–123 (2005)CrossRefGoogle Scholar
  24. 24.
    Guo, G., Lai, A.: A Survey on Still Image Based Human Action Recognition. Pattern Recognition 47, 3343–3361 (2014)CrossRefGoogle Scholar
  25. 25.
    Guan, N., Tao, D., Luo, Z., Yuan, B.: Manifold Regularized Discriminative Non-negative Matrix Factorization with Fast Gradient Descent. IEEE Transactions on Image Processing 20(7), 2030–2048 (2011)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Guan, N., Tao, D., Luo, Z., Yuan, B.: Non-negative Patch Alignment Framework. IEEE Transactions on Neural Networks 22(8), 1218–1230 (2011)CrossRefGoogle Scholar
  27. 27.
    Li, K., Fu, Y.: Prediction of Human Activity by Discovering Temporal Sequence Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(8), 1644–1657 (2014)CrossRefGoogle Scholar
  28. 28.
    Kong, Y., Jia, Y., Fu, Y.: Interactive Phrases: Semantic Descriptions for Human Interaction Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 36(9), 1775–1788 (2014)CrossRefGoogle Scholar
  29. 29.
    Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)CrossRefGoogle Scholar
  30. 30.
    Lambrecht, J., Kleinsorge, M., Rosenstrauch, M., Krger, J.: Spatial Programming for Industrial Robots Through Task Demonstration. International Journal of Advanced Robotic Systems 10(254) (2013)Google Scholar
  31. 31.
    Danafar, S., Gheissari, N.: Action recognition for surveillance applications using optic flow and SVM. In: Asian Conference on Computer Vision, pp. 457–466 (2007)Google Scholar
  32. 32.
    Lowe, D.G.: Distinctive Image Features from Scale-invariant Points. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  33. 33.
    Guan, N., Tao, D., Luo, Z., Yuan, B.: NeNMF: An Optimal Gradient Method for Non-negative Matrix Factorization. IEEE Transactions on Signal Processing 60(6), 2882–2898 (2012)MathSciNetCrossRefGoogle Scholar
  34. 34.
    van de Seijer, J., Schmid, C., Verbeek, J.J., Larlus, D.: Learning Color Names for Real-world Applications. IEEE Transactions on Image Processing 18(7), 1512–1524 (2009)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Guo, G., Lai, A.: A survey on still image based human action recognition. Pattern Recognition 47(10), 3343–3361 (2014)CrossRefGoogle Scholar
  36. 36.
    Yao, B., Jiang, X., Khosla, A., Lin, A.L., Guibas, L.J., Fei-Fei, L.: Human action recognition by learning bases of action attributes and parts. In: International Conference on Computer Vision, Barcelona, Spain, 6–13 November 2011Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Naiyang Guan
    • 1
    Email author
  • Dacheng Tao
    • 2
  • Long Lan
    • 1
  • Zhigang Luo
    • 1
  • Xuejun Yang
    • 3
  1. 1.Science and Technology on Parallel and Distributed Processing LaboratoryCollege of Computer, National University of Defense TechnologyChangshaPeople’s Republic of China
  2. 2.Centre for Quantum Computation and Intelligent Systems and the Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia
  3. 3.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaPeople’s Republic of China

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