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Multimodal PLSA for Movie Genre Classification

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Multiple Classifier Systems (MCS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9132))

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Abstract

The aim of this paper is to categorize movies into genres using the previews. Our study attempts to combine audio, visual and text features to classify a collection of movie previews into action, biography, comedy, and horror. For each of the collected previews, the audio and visual features are extracted and the text features are drawn from social tags via social websites. The probabilistic latent semantic analysis (PLSA) is used to incorporate the features from these three different aspects of information. The standard PLSA processes one type of information only. Therefore double-model and triple-model PLSAs are extended in order to combine two or three different types of information. We compare these various variants of PLSA approaches with unimodal PLSAs, which use either audio, visual or text features only. The experimental results show not only that one of the triple-model PLSAs achieves the highest accuracy, but also that social tags (text features) play an important role for classifying movies genres.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City, 24205, Taiwan

    Hao-Zhi Hong & Jen-Ing G. Hwang

Authors
  1. Hao-Zhi Hong
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  2. Jen-Ing G. Hwang
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Corresponding author

Correspondence to Jen-Ing G. Hwang .

Editor information

Editors and Affiliations

  1. Ulm University, Ulm, Germany

    Friedhelm Schwenker

  2. University of Cagliari, Cagliari, Italy

    Fabio Roli

  3. University of Surrey, Guildford, United Kingdom

    Josef Kittler

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Hong, HZ., Hwang, JI.G. (2015). Multimodal PLSA for Movie Genre Classification. In: Schwenker, F., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2015. Lecture Notes in Computer Science(), vol 9132. Springer, Cham. https://doi.org/10.1007/978-3-319-20248-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-20248-8_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20247-1

  • Online ISBN: 978-3-319-20248-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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