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Combining Short and Long Term Audio Features for TV Sports Highlight Detection

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Advances in Information Retrieval (ECIR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3936))

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Abstract

As bearer of high-level semantics, audio signal is being more and more used in content-based multimedia retrieval. In this paper, we investigate TV tennis game highlight detection based on the use of both short and long term audio features and propose two approaches, decision fusion and hierarchical classifier, in order to combine these two kinds of audio features. As more information is included in decision making, the overall performance of the system is enhanced.

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References

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Author information

Authors and Affiliations

  1. Tsinghua University, Beijing, 100084, China

    Bin Zhang & Weibei Dou

  2. LIRIS CNRS UMR 5205, Ecole Centrale de Lyon, France

    Liming Chen

Authors
  1. Bin Zhang
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  2. Weibei Dou
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  3. Liming Chen
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Editor information

Editors and Affiliations

  1. Queen Mary, University of London, London, UK

    Mounia Lalmas

  2. Department of Information Science, City University, Northampton Square, EC1V OHB, London, UK

    Andy MacFarlane

  3. Knowledge Media Institute, The Open University, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  4. Queen Mary University of London, UK

    Anastasios Tombros

  5. CWI, Amsterdam, The Netherlands

    Theodora Tsikrika

  6. Department of Computing, Imperial College London, South Kensington Campus, SW7 2AZ, London, UK

    Alexei Yavlinsky

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhang, B., Dou, W., Chen, L. (2006). Combining Short and Long Term Audio Features for TV Sports Highlight Detection. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds) Advances in Information Retrieval. ECIR 2006. Lecture Notes in Computer Science, vol 3936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11735106_44

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  • DOI: https://doi.org/10.1007/11735106_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33347-0

  • Online ISBN: 978-3-540-33348-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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