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Human action recognition using modified slow feature analysis and multiple kernel learning

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Abstract

A novel human action recognition method is proposed, which includes two periods of action feature extraction and action recognition. Firstly, we use a modified slow feature analysis (SFA) to extract video local feature. Unlike slow feature analysis, we redefine the objective function with supervised information, which make the modified SFA more suitable to preserve the slow feature and label information. Meanwhile, in effort to cope with the dimension explosion in SFA, locality preserving projections (LPP) is used to reduce the quadratic expansion dimension. Secondly, we use a multiple kernel learning method (MKL) to classify human action, in which the weights of different kernels are optimized by combining Bacterial Chemotaxis method and Powell method. The results of experiments indicate the efficiency of our method.

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Acknowledgments

This work is supported by the Postdoctoral Science Foundation of Central South University, the Construct Program of the Key Discipline in Hunan Province, Hunan Province Education and Science Issue “Performance Evaluation for College Teacher Based on Adaptive Learning” (no. XJK013CGD083), the Teaching Reform Research Foundation of Hunan Province Ordinary College under Grant (no. [2014]247-612), and the Research Foundation of Science & Technology Office of Hunan Province under Grant (no. 2014FJ3057).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Yongliang Xiao & Limin Xia

  2. Department of Information Management, Hunan University of Finance and Economics, Changsha, 410205, China

    Yongliang Xiao

Authors
  1. Yongliang Xiao
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  2. Limin Xia
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Correspondence to Limin Xia.

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Xiao, Y., Xia, L. Human action recognition using modified slow feature analysis and multiple kernel learning. Multimed Tools Appl 75, 13041–13056 (2016). https://doi.org/10.1007/s11042-015-2569-6

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  • DOI: https://doi.org/10.1007/s11042-015-2569-6

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