You are not logged in.

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

Phung, Dinh Q., Bui, Hung H. and Venkatesh, Svetha 2004, Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models. In Fred, Ana, Caelli, Terry, Duin, Robert P.W., Campilho, Aurélio and de Ridder, Dick (ed), Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings, Springer-Verlag, Berlin, Germany, pp.1155-1163, doi: 10.1007/978-3-540-27868-9_127.


Title Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models
Author(s) Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Title of book Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings
Editor(s) Fred, Ana
Caelli, Terry
Duin, Robert P.W.
Campilho, Aurélio
de Ridder, Dick
Publication date 2004
Series Lecture notes in computer science ; 3138
Chapter number 127
Total chapters 127
Start page 1155
End page 1163
Total pages 9
Publisher Springer-Verlag
Place of Publication Berlin, Germany
Keyword(s) video data
multimedia indexing
educational videos
Summary In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video -educational videos - in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its subunits. We model the hierarchy of topical structures by an HHMM and demonstrate the usefulness of the model in detecting topic transitions.
ISBN 9783540225706
3540225706
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-540-27868-9_127
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 B1.1 Book chapter
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044662

Document type: Book Chapter
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 276 Abstract Views  -  Detailed Statistics
Created: Fri, 20 Apr 2012, 13:23:57 EST

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.