VIDEO FRAME IDENTIFICATION FOR LEARNING MEDIA CONTENT UNDERSTANDING (FriAmOR5)

Author(s) : Ying Li (IBM T. J. Watson Research Center, United States of America) Chitra Dorai (IBM T. J. Watson Research Center, United States of America)

Abstract : This paper presents our latest work on identifying frame content types for understanding learning media content. In particular, we categorize frames into six classes namely, slide, web-page, instructor, audience, picture-in-picture and miscellaneous, which make up salient narrative modes in learning videos. Various image and video analysis approaches are explored to achieve this task. Preliminary experiments carried out on three recorded seminars have yielded encouraging results. The identification of fine-grained visual content types can assist us in content understanding, access, browsing and searching of generic learning videos.

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