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Cognitively Motivated Novelty Detection in Video Data Streams

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Multimedia Data Mining and Knowledge Discovery

Abstract

Automatically detecting novel events in video data streams is an extremely challenging task. In recent years, machine-based parametric learning systems have been quite successful in exhaustively capturing novelty in video if the novelty filters are well-defined in constrained environments.

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Authors
  1. James M. Kang
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  2. Muhammad Aurangzeb Ahmad
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  3. Ankur Teredesai
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  4. Roger Gaborski
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Editor information

Editors and Affiliations

  1. Accenture Technology Labs, Accenture Ltd, 161 N. Clark St., 60601, Chicago, IL, USA

    Valery A. Petrushin MS, PhD

  2. School of Forest Resources and Conservation, University of Florida, EC 31 2601 N. Floyd Rd., 75080-1407, Richardson, TX, USA

    Latifur Khan BS, MS, PhD

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Kang, J.M., Aurangzeb Ahmad, M., Teredesai, A., Gaborski, R. (2007). Cognitively Motivated Novelty Detection in Video Data Streams. In: Petrushin, V.A., Khan, L. (eds) Multimedia Data Mining and Knowledge Discovery. Springer, London. https://doi.org/10.1007/978-1-84628-799-2_11

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  • DOI: https://doi.org/10.1007/978-1-84628-799-2_11

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-436-6

  • Online ISBN: 978-1-84628-799-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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