Narrative structure analysis of lecture video with hierarchical hidden markov model for e-learning

Authors:
Yu-Chi Liu;Xi-Dao Luan;Yu-Xiang Xie;Duan-Hui Dai;Ling-Da Wu
Affiliations:
School of Information System and Management, National University of Defense Technology, Changsha, China;School of Information System and Management, National University of Defense Technology, Changsha, China;School of Information System and Management, National University of Defense Technology, Changsha, China;Center of Simulation Training of Army Aviation Institute, Beijing, China;School of Information System and Management, National University of Defense Technology, Changsha, China
Venue:
Edutainment'06 Proceedings of the First international conference on Technologies for E-Learning and Digital Entertainment
Year:
2006

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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.