A Framework for Automatic Topic Segmentation in Video Lectures

Nowadays, video lectures are a very popular way to transmit knowledge, and because of that, there are many repositories with a large catalog of those videos on web. Despite all benefits that this high availability of video lectures brings, some problems also emerge from this scenario. One of these problems is that, it is very difficult find relevant content associate with those videos. Many times, students must to watch the entire video lecture to find the point of interest and, sometimes, these points are not found. For that reason, the proposal of this master’s project is to investigate and propose a novel framework based on early fusion of low and high-level audio features enriched with external knowledge from open databases for automatic topic segmentation in video lectures. We have performed preliminary experiments in two sets of video lectures using the current state of our work. The obtained results were very satisfactory, which evidences the potential of our proposal.