Detecting Significant Events in Lecture Video using Supervised Machine Learning

This paper describes work we are doing to identify significant events in video captures of academic lectures. Unlike other approaches which tend to define per-image comparison threshold values based on intuition or empirically derived results, we use supervised machine teaming techniques to automatically determine appropriate image characteristics based on end-users understanding of what constitutes an important event. This makes our approach more adaptable to different kinds of content, and still provides a substantial level of agreement with human experts.