Automatically learning structural units in educational videos with the hierarchical hidden Markov models Phung, Dinh Q., Venkatesh, Svetha and Bui, Hung H. 2004, Automatically learning structural units in educational videos with the hierarchical hidden Markov models, in ICIP 2004 : Proceedings of the 2004 International Conference on Image Processing, IEEE, Piscataway, N.J., pp. 1605-1608, doi: 10.1109/ICIP.2004.1421375. Attached Files Name Description MIMEType Size Downloads venkatesh-automaticallylearning-2004.pdf Published version application/pdf 311.96KB 48 Title Automatically learning structural units in educational videos with the hierarchical hidden Markov models Author(s) Phung, Dinh Q. orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Bui, Hung H. Conference name International Conference on Image Processing (2004 : Singapore) Conference location Singapore Conference dates 24-27 Oct. 2004 Title of proceedings ICIP 2004 : Proceedings of the 2004 International Conference on Image Processing Editor(s) [Unknown] Publication date 2004 Conference series International Conference on Image Processing Start page 1605 End page 1608 Total pages 4 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) Australia content management data mining electronic learning event detection hidden Markov models indexing layout motion pictures videos Summary In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations. Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ISBN 9780780385542 0780385543 ISSN 1522-4880 Language eng DOI 10.1109/ICIP.2004.1421375 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 ©2004, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044633 Document type: Conference Paper Collections: School of Information Technology Open Access Collection 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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 20 Apr 2012, 11:36:34 EST Fri, 27 Apr 2012, 10:32:13 EST Fri, 27 Apr 2012, 10:34:19 EST Fri, 27 Apr 2012, 10:38:14 EST Thu, 03 May 2012, 13:27:48 EST Tue, 06 Sep 2016, 18:47:06 EST Sat, 21 Oct 2017, 02:03:56 EST Tue, 05 Dec 2017, 12:31:38 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 333 Abstract Views, 48 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:36:34 EST

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