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

Dinh Q. Phung, Hung Hai Bui, Svetha Venkatesh

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.