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. Attached Files Name Description MIMEType Size Downloads Title High level segmentation of instructional videos based on content density Author(s) Phung, Dinh Q. orcid.org/0000-0002-9977-8247 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 Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 20 Apr 2012, 11:37:24 EST Fri, 27 Apr 2012, 12:12:46 EST Fri, 27 Apr 2012, 12:14:12 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 11 times in Scopus Search Google Scholar Access Statistics: 218 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:37:24 EST

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