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.
Conference Committee Involvement (6)
Multimedia Content Access: Algorithms and Systems IV
21 January 2010 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems III
21 January 2009 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems II
30 January 2008 | San Jose, California, United States
Multimedia Content Access: Algorithms and Systems
31 January 2007 | San Jose, CA, United States
Multimedia Content Analysis, Management, and Retrieval 2006
18 January 2006 | San Jose, California, United States
Multimedia Computing and Networking 2005
19 January 2005 | San Jose, California, United States
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