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Cognitively Motivated Novelty Detection in Video Data Streams

  • James M. Kang
  • Muhammad Aurangzeb Ahmad
  • Ankur Teredesai
  • Roger Gaborski

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.

Keywords

Video Sequence Video Stream Comic Book Novelty Detection Video Indexing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2007

Authors and Affiliations

  • James M. Kang
  • Muhammad Aurangzeb Ahmad
  • Ankur Teredesai
  • Roger Gaborski

There are no affiliations available

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