You are not logged in.
Openly accessible

Narrative structure analysis with education and training videos for e-learning

Phung, Quoc Dinh, Dorai, Chitra and Venkatesh, Svetha 2002, Narrative structure analysis with education and training videos for e-learning, in ICPR 2002 : Proceedings of the 16th International Conference on Pattern Recognition, IEEE, Los Alamitos, Calif., pp. 835-838, doi: 10.1109/ICPR.2002.1048432.

Attached Files
Name Description MIMEType Size Downloads
venkatesh-narrativestructure-2002.pdf Published version application/pdf 286.28KB 167

Title Narrative structure analysis with education and training videos for e-learning
Author(s) Phung, Quoc DinhORCID iD for Phung, Quoc Dinh orcid.org/0000-0002-9977-8247
Dorai, Chitra
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Pattern Recognition (16th : 2002 : Quebec, Canada)
Conference location Quebec, Canada
Conference dates 11-15 Aug. 2002
Title of proceedings ICPR 2002 : Proceedings of the 16th International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2002
Conference series International Conference on Pattern Recognition
Start page 835
End page 838
Total pages 4
Publisher IEEE
Place of publication Los Alamitos, Calif.
Keyword(s) classification (of information)
distance education
hierarchical systems
mathematical models
problem solving
set theory
Summary This paper deals with the problem ofstructuralizing education and training videos for high-level semantics extraction and nonlinear media presentation in e-learning applications. Drawing guidance from production knowledge in instructional media, we propose six main narrative structures employed in education and training videos for both motivation and demonstration during learning and practical training. We devise a powerful audiovisual feature set, accompanied by a hierarchical decision tree-based classification system to determine and discriminate between these structures. Based on a two-liered hierarchical model, we demonstrate that we can achieve an accuracy of 84.7% on a comprehensive set of education and training video data.
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.
ISSN 1051-4651
Language eng
DOI 10.1109/ICPR.2002.1048432
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, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044651

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
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
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 5 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 320 Abstract Views, 167 File Downloads  -  Detailed Statistics
Created: Fri, 20 Apr 2012, 11:37:33 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.