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Video frame classification for learning media content understanding

This paper presents our latest work on analyzing and understanding the content of learning media such as instructional and training videos, based on the identification of video frame types. In particular, we achieve this goal by first partitioning a video sequence into homogeneous segments where each segment contains frames of the same image type such as slide or web-page; then we categorize the frames within each segment into one of the following four classes: slide, web-page, instructor and picture-in-picture, by analyzing various visual and text features. Preliminary experiments carried out on two seminar talks have yielded encouraging results. It is our belief that by classifying video frames into semantic image categories, we are able to better understand and annotate the learning media content and subsequently facilitate its content access, browsing and retrieval.