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Tucker decomposition-based tensor learning for human action recognition

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Abstract

The spatial information is the important cue for human action recognition. Different from the vector representation, the spatial structure of human action in the still images can be preserved by the tensor representation. This paper proposes a robust human action recognition algorithm by tensor representation and Tucker decomposition. In this method, the still image containing human action is represented by a tensor descriptor (Histograms of Oriented Gradients). This representation preserves the spatial information inside the human action. Based on this representation, the unknown tensor parameter is decomposed according to the Tucker tensor decomposition at first, and then the optimization problems can be solved using the alternative optimization method, where at each iteration, the tensor descriptor is projected along one order and the parameter along the corresponding order can be estimated by solving the Ridge Regression problem. The estimated tensor parameter is more discriminative because of effectively using the spacial information along each order. Experiments are conducted using action images from three publicly available databases. Experimental results demonstrate that our method outperforms other methods.

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Acknowledgments

This work was partly supported by the National Program on the Key Basic Research Project (under Grant 2013CB329301), NSFC (under Grant 61202166, 61472276), the Major Project of National Social Science Fund (under Grant 14ZDB153) and Doctoral Fund of Ministry of Education of China (under Grant 20120032120042).

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Jianguang Zhang & Yahong Han

  2. Department of Mathematics and Computer Science, Hengshui University, Hengshui, China

    Jianguang Zhang

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China

    Yahong Han

  4. School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Jianmin Jiang

Authors
  1. Jianguang Zhang
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  2. Yahong Han
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  3. Jianmin Jiang
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Corresponding author

Correspondence to Yahong Han.

Additional information

Communicated by B. Prabhakaran.

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Zhang, J., Han, Y. & Jiang, J. Tucker decomposition-based tensor learning for human action recognition. Multimedia Systems 22, 343–353 (2016). https://doi.org/10.1007/s00530-015-0464-7

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  • DOI: https://doi.org/10.1007/s00530-015-0464-7

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