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.
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Phung, D.Q., Bui, H.H., Venkatesh, S. (2004). Content Structure Discovery in Educational Videos Using Shared Structures in the Hierarchical Hidden Markov Models. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_127
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DOI: https://doi.org/10.1007/978-3-540-27868-9_127
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