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Action Recognition Based on Maximum Entropy Fuzzy Clustering Algorithm

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Foundations of Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 277))

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Abstract

Considering the problems that initial clustering center selection is very sensitive to clustering results, it is easy to fall into local optimum and only globular clusters can be found for the k-means algorithm in human action recognition. This paper presents a method based on maximum entropy fuzzy clustering and a new interest points detecting method for human action recognition. Firstly, the interest points of the videos are detected by the new approach, and the 3D-SIFT features of the points are extracted, and then, the features of the training videos are clustered generating video words based on maximum entropy fuzzy clustering algorithm to construct the codebook. In the end, the histogram based on the codebook for every video is built, which is used to classify targets in the SVM multi-class classifier. Experimental results show that the proposed method can effectively improve the expression ability of the codebook and the efficiency of the recognition of human action.

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Author information

Authors and Affiliations

  1. School of Computer and Information Science, Southwest University, Chongqing, China

    Xiaoqin Tang & Guoqiang Xiao

Authors
  1. Xiaoqin Tang
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  2. Guoqiang Xiao
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Corresponding author

Correspondence to Guoqiang Xiao .

Editor information

Editors and Affiliations

  1. College of Computer and Software Engineering, Shenzhen University, Shenzhen, China

    Zhenkun Wen

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

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© 2014 Springer-Verlag Berlin Heidelberg

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Tang, X., Xiao, G. (2014). Action Recognition Based on Maximum Entropy Fuzzy Clustering Algorithm. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-54924-3_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54923-6

  • Online ISBN: 978-3-642-54924-3

  • eBook Packages: EngineeringEngineering (R0)

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