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Shot Type Classification in Sports Video Using Fuzzy Information Granular

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3682))

Abstract

In this paper, we present a new method for classifying shot type in sports video using fuzzy information granular. The problem is important for applications such as video structure analysis and content understanding. In particular,two-stage off-line learning processes perform knowledge extraction of semantic concepts and automatic shot classification, respectively. In the first stage, the extracted prominent regions are used as a good pattern in semantic concept level. Then a number of global features are defined as efficient input of the shot type classifier in the second stage. The identification of semantic concepts and classification of shot are based on soft decisions. Hence, this framework can adequately capture the uncertainty or ambiguity of scales of a shot. Experimental results show the excellent performance of the approach.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Beijing Jiaotong University, Beijing, 100044, P.R. China

    Congyan Lang, De Xu, Wengang Cheng & Yiwei Jiang

Authors
  1. Congyan Lang
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  2. De Xu
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  3. Wengang Cheng
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  4. Yiwei Jiang
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Lang, C., Xu, D., Cheng, W., Jiang, Y. (2005). Shot Type Classification in Sports Video Using Fuzzy Information Granular. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_168

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  • DOI: https://doi.org/10.1007/11552451_168

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28895-4

  • Online ISBN: 978-3-540-31986-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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