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High level segmentation of instructional videos based on content density

Phung, Dinh Q., Venkatesh, Svetha and Dorai, Chitra 2002, High level segmentation of instructional videos based on content density, in MULTIMEDIA 2002 : Proceedings of the 10th ACM International Multimedia Conference and Exhibition, ACM, New York, N. Y., pp. 295-298, doi: 10.1145/641007.641068.

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Title High level segmentation of instructional videos based on content density
Author(s) Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Dorai, Chitra
Conference name ACM International Conference on Multimedia (10th : 2002 : Juan-les-Pins, France)
Conference location Juan-les-Pins, France
Conference dates 1-6 Dec. 2002
Title of proceedings MULTIMEDIA 2002 : Proceedings of the 10th ACM International Multimedia Conference and Exhibition
Editor(s) [Unknown]
Publication date 2002
Conference series ACM International Conference on Multimedia
Start page 295
End page 298
Total pages 4
Publisher ACM
Place of publication New York, N. Y.
Keyword(s) content based retrieval
image segmentation
indexing (of information)
probability density function
Summary Automatically partitioning instructional videos into topic sections is a challenging problem in e-learning environments for efficient content management and cataloging. This paper addresses this problem by proposing a novel density function to delineate sections underscored by changes in topics in instructional and training videos. The content density function draws guidance from the observation that topic boundaries coincide with the ebb and flow of the 'density' of content shown in these videos. Based on this function, we propose two methods for high-level segmentation by determining topic boundaries. We study the performance of the two methods on eight training videos, and our experimental results demonstrate the effectiveness and robustness of the two proposed high-level segmentation algorithms for learning media.
ISBN 158113620X
Language eng
DOI 10.1145/641007.641068
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2002, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044648

Document type: Conference Paper
Collection: School of Information Technology
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