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Narrative Structure Analysis of Lecture Video with Hierarchical Hidden Markov Model for E-Learning

  • Yu-Chi Liu
  • Xi-Dao Luan
  • Yu-Xiang Xie
  • Duan-Hui Dai
  • Ling-Da Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3942)

Abstract

In E-learning, structure analysis of lecture video is the first step for effective and efficient indexing, browsing and retrieval. A hierarchical model of narrative structure for lecture video is introduced. The root is lecture video; the next is layer of narrative elements conveying meaningful information in semantics; then is narrative features layer closely to both visual and auditory physical features. A framework is proposed to analyze narrative structure. Extraction of narrative features is described as well. Hierarchical hidden Markov model is introduced to determine the parameters and detect narrative elements automatically.

Keywords

Audio Signal Dynamic Bayesian Network Narrative Structure Lecture Video Narrative Element 
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 2006

Authors and Affiliations

  • Yu-Chi Liu
    • 1
    • 2
  • Xi-Dao Luan
    • 1
  • Yu-Xiang Xie
    • 1
  • Duan-Hui Dai
    • 3
  • Ling-Da Wu
    • 1
  1. 1.School of Information System and ManagementNational University of Defense TechnologyChangshaChina
  2. 2.Radar AcademyWuhanChina
  3. 3.Center of Simulation Training of Army Aviation InstituteBeijingChina

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