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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 3–18Cite as

Sparse Code Filtering for Action Pattern Mining

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10112))

Abstract

Action recognition has received increasing attention during the last decade. Various approaches have been proposed to encode the videos that contain actions, among which self-similarity matrices (SSMs) have shown very good performance by encoding the dynamics of the video. However, SSMs become sensitive when there is a very large view change. In this paper, we tackle the multi-view action recognition problem by proposing a sparse code filtering (SCF) framework which can mine the action patterns. First, a class-wise sparse coding method is proposed to make the sparse codes of the between-class data lie close by. Then we integrate the classifiers and the class-wise sparse coding process into a collaborative filtering (CF) framework to mine the discriminative sparse codes and classifiers jointly. The experimental results on several public multi-view action recognition datasets demonstrate that the presented SCF framework outperforms other state-of-the-art methods.

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Author information

Authors and Affiliations

  1. University of Trento, Trento, Italy

    Wei Wang, Yan Yan & Nicu Sebe

  2. National University of Singapore, Singapore, Singapore

    Liqiang Nie

  3. Advanced Digital Sciences Center, Singapore, Singapore

    Stefan Winkler

  4. Hefei University of Technology, Hefei, China

    Luming Zhang

Authors
  1. Wei Wang
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  2. Yan Yan
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  3. Liqiang Nie
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  4. Luming Zhang
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  5. Stefan Winkler
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  6. Nicu Sebe
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Corresponding author

Correspondence to Wei Wang .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Wang, W., Yan, Y., Nie, L., Zhang, L., Winkler, S., Sebe, N. (2017). Sparse Code Filtering for Action Pattern Mining. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-54184-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54183-9

  • Online ISBN: 978-3-319-54184-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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