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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.

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Title Automatically learning structural units in educational videos with the hierarchical hidden Markov models
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
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
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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.