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Content-Based Discovery of Multiple Structures from Episodes of Recurrent TV Programs Based on Grammatical Inference

  • Bingqing Qu
  • Félicien Vallet
  • Jean Carrive
  • Guillaume Gravier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8935)

Abstract

TV program structuring is essential for program indexing and retrieval. Practically, various types of programs lead to a diversity of program structures. Besides, several episodes of a recurrent program might exhibit different structures. Previous work mostly relies on supervised approaches by adopting prior knowledge about program structures. In this paper, we address the problem of unsupervised program structuring with minimal domain knowledge. We propose an approach to identify multiple structures and infer structural grammars for recurrent TV programs of different types. It involves three sub-problems: i) we determine the structural elements contained in programs with minimal knowledge about which type of elements may be present; ii) we identify multiple structures for the programs if any and model the structures; iii) we generate the structural grammar for each corresponding structure. Finally, we conduct use cases on real recurrent programs of three different types to demonstrate the effectiveness of proposed approach.

Keywords

Coherent State Time Stamp Program Structure Multiple Structure Distribution Matrix 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Bingqing Qu
    • 1
    • 3
  • Félicien Vallet
    • 3
  • Jean Carrive
    • 3
  • Guillaume Gravier
    • 2
  1. 1.University of Rennes 1 – IRISA and Inria RennesFrance
  2. 2.CNRS – IRISA and Inria RennesFrance
  3. 3.French National Audiovisual InstituteFrance

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