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.
Supported by the Natural Science Fund of China(No.60473117).
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© 2006 Springer-Verlag Berlin Heidelberg
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Liu, YC., Luan, XD., Xie, YX., Dai, DH., Wu, LD. (2006). Narrative Structure Analysis of Lecture Video with Hierarchical Hidden Markov Model for E-Learning. In: Pan, Z., Aylett, R., Diener, H., Jin, X., Göbel, S., Li, L. (eds) Technologies for E-Learning and Digital Entertainment. Edutainment 2006. Lecture Notes in Computer Science, vol 3942. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736639_54
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DOI: https://doi.org/10.1007/11736639_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33423-1
Online ISBN: 978-3-540-33424-8
eBook Packages: Computer ScienceComputer Science (R0)