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Proceedings Paper

Video frame classification for learning media content understanding
Author(s): Ying Li; Chitra Dorai
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Paper Abstract

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.

Paper Details

Date Published: 25 October 2004
PDF: 12 pages
Proc. SPIE 5601, Internet Multimedia Management Systems V, (25 October 2004); doi: 10.1117/12.570053
Show Author Affiliations
Ying Li, IBM Thomas J. Watson Research Ctr. (United States)
Chitra Dorai, IBM Thomas J. Watson Research Ctr. (United States)

Published in SPIE Proceedings Vol. 5601:
Internet Multimedia Management Systems V
John R. Smith; Tong Zhang; Sethuraman Panchanathan, Editor(s)

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