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Multilinear Methods for Spatio-Temporal Image Recognition

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10424))

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Abstract

We introduce multilinear dimension-reduction and classification methods for video image sequences. Tensor-to-tensor projection methods for spatio-temporal data are derived as dimension-reduction methods using the three-mode tensor representation. The tensor-to-tensor projection methods transform a tensor to a product of smaller tensors. Furthermore, we construct efficient and robust multiclass classifiers for multilinear forms by using tensorial expressions of spatio-temporal video sequences.

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Author information

Authors and Affiliations

  1. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Hayato Itoh

  2. School of Advanced Integration Science, Chiba University, Chiba, Japan

    Hayato Itoh

  3. Institute of Management and Information Technologies, Chiba University, Chiba, Japan

    Atsushi Imiya

  4. Graduate School of Engineering, Nagasaki University, Nagasaki, Japan

    Tomoya Sakai

Authors
  1. Hayato Itoh
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  2. Atsushi Imiya
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  3. Tomoya Sakai
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Corresponding author

Correspondence to Hayato Itoh .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Michael Felsberg

  2. Lund University, Lund, Sweden

    Anders Heyden

  3. University of Southern Denmark, Odense, Denmark

    Norbert Krüger

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Itoh, H., Imiya, A., Sakai, T. (2017). Multilinear Methods for Spatio-Temporal Image Recognition. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10424. Springer, Cham. https://doi.org/10.1007/978-3-319-64689-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-64689-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64688-6

  • Online ISBN: 978-3-319-64689-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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