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A Comparative Study of Encoding, Pooling and Normalization Methods for Action Recognition

  • Xingxing Wang
  • LiMin Wang
  • Yu Qiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

Bag of visual words (BoVW) models have been widely and successfully used in video based action recognition. One key step in constructing BoVW representation is to encode feature with a codebook. Recently, a number of new encoding methods have been developed to improve the performance of BoVW based object recognition and scene classification, such as soft assignment encoding [1], sparse encoding [2], locality-constrained linear encoding [3] and Fisher kernel encoding [4]. However, their effects for action recognition are still unknown. The main objective of this paper is to evaluate and compare these new encoding methods in the context of video based action recognition. We also analyze and evaluate the combination of encoding methods with different pooling and normalization strategies. We carry out experiments on KTH dataset [5] and HMDB51 dataset [6]. The results show the new encoding methods can significantly improve the recognition accuracy compared with classical VQ. Among them, Fisher kernel encoding and sparse encoding have the best performance. By properly choosing pooling and normalization methods, we achieve the state-of-the-art performance on HMDB51.

Keywords

Gaussian Mixture Model Visual Word Action Recognition Sparse Code Code Word 
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.

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References

  1. 1.
    Liu, L., Wang, L., Liu, X.: In defense of soft-assignment coding. In: ICCV, pp. 2486–2493 (2011)Google Scholar
  2. 2.
    Yang, J., Yu, K., Gong, Y., Huang, T.S.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp. 1794–1801 (2009)Google Scholar
  3. 3.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T.S., Gong, Y.: Locality-constrained linear coding for image classification. In: CVPR, pp. 3360–3367 (2010)Google Scholar
  4. 4.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: ICPR, vol. 3, pp. 32–36 (2004)Google Scholar
  6. 6.
    Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: Hmdb: A large video database for human motion recognition. In: ICCV, pp. 2556–2563 (2011)Google Scholar
  7. 7.
    Aggarwal, J., Ryoo, M.: Human activity analysis: A review. ACM Comput. Surv. 43, 1–43 (2011)CrossRefGoogle Scholar
  8. 8.
    Turaga, P.K., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: A survey. TCSVT, 1473 –1488 (2008)Google Scholar
  9. 9.
    Niebles, J.C., Chen, C.-W., Fei-Fei, L.: Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 392–405. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Tang, K., Fei-Fei, L., Koller, D.: Learning latent temporal structure for complex event detection. In: CVPR, pp. 1250–1257 (2012)Google Scholar
  11. 11.
    Marszalek, M., Laptev, I., Schmid, C.: Actions in context. In: CVPR, pp. 2929–2936 (2009)Google Scholar
  12. 12.
    Kovashka, A., Grauman, K.: Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In: CVPR, pp. 2046–2053 (2010)Google Scholar
  13. 13.
    Brendel, W., Todorovic, S.: Learning spatiotemporal graphs of human activities. In: ICCV, pp. 778–785 (2011)Google Scholar
  14. 14.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV Workshop on Statistical Learning in Computer Vision, pp. 1–22 (2004)Google Scholar
  15. 15.
    van Gemert, J.C., Geusebroek, J.-M., Veenman, C.J., Smeulders, A.W.M.: Kernel Codebooks for Scene Categorization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 696–709. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)Google Scholar
  17. 17.
    Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. IJCV 73, 213–238 (2007)CrossRefGoogle Scholar
  18. 18.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. IJCV 88, 303–338 (2010)CrossRefGoogle Scholar
  19. 19.
    Laptev, I.: On space-time interest points. IJCV 64, 107–123 (2005)CrossRefGoogle Scholar
  20. 20.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: 2005 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72 (2005)Google Scholar
  21. 21.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)Google Scholar
  22. 22.
    Bishop, C.M.: Pattern Recognition and Machiner Learning. Springer (2006)Google Scholar
  23. 23.
    Johnson, S.: Hierarchical clustering schemes. Psychometrika 32, 241–254 (1967)CrossRefGoogle Scholar
  24. 24.
    Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS, vol. (2), pp. 849–856.Google Scholar
  25. 25.
    Jaakkola, T., Haussler, D.: Exploiting generative models in discriminative classifiers. In: NIPS, pp. 487–493 (1998)Google Scholar
  26. 26.
    Boureau, Y., Ponce, J., LeCun, Y.: A theoretical analysis of feature pooling in visual recognition. In: ICML (2010)Google Scholar
  27. 27.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  28. 28.
    Sadanand, S., Corso, J.: Action bank: A high-level representation of activity in video. In: CVPR, pp. 1234–1241 (2012)Google Scholar
  29. 29.
    Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In: ICCV, pp. 1593–1600 (2009)Google Scholar
  30. 30.
    Liu, J., Kuipers, B., Savarese, S.: Recognizing human actions by attributes. In: CVPR, pp. 3337–3344 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xingxing Wang
    • 1
  • LiMin Wang
    • 1
    • 2
  • Yu Qiao
    • 1
    • 2
  1. 1.Shenzhen Key lab of CVPR, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Department of Information EngineeingThe Chinese University of Hong KongChina

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