Detecting Significant Events in Lecture Video using Supervised Machine Learning
Christopher H. Brooks, Kristofor Amundson, Jim E. Greer · Frontiers in artificial intelligence and applications · 2009
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 learning 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.