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Content Structure Discovery in Educational Videos Using Shared Structures in the Hierarchical Hidden Markov Models

  • Dinh Q. Phung
  • Hung H. Bui
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

In this paper, we present an application of the hierarchical hmm for structure discovery in educational videos. The hhmm has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video – educational videos – in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its sub-units. We model the hierarchy of topical structures by an hhmm and demonstrate the usefulness of the model in detecting topic transitions.

Keywords

Hide Markov Model Semantic Concept Educational Video Broadcast News Shared Structure 
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-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dinh Q. Phung
    • 1
  • Hung H. Bui
    • 2
  • Svetha Venkatesh
    • 1
  1. 1.Department of ComputingCurtin University of TechnologyPerth
  2. 2.Artificial Intelligence CenterSRI InternationalMenlo ParkUSA

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