Topic transition detection using hierarchical hidden Markov and semi-Markov models Phung, Dinh Q., Duong, T. V., Venkatesh, S. and Bui, Hung H. 2005, Topic transition detection using hierarchical hidden Markov and semi-Markov models, in MM'05 : Proceedings of the 13th ACM International Conference on Multimedia, Association for Computing Machinery, New York, N. Y., pp. 11-20, doi: 10.1145/1101149.1101153. Attached Files Name Description MIMEType Size Downloads Title Topic transition detection using hierarchical hidden Markov and semi-Markov models Author(s) Phung, Dinh Q. orcid.org/0000-0002-9977-8247 Duong, T. V. Venkatesh, S. orcid.org/0000-0001-8675-6631 Bui, Hung H. Conference name ACM International Conference on Multimedia (13th : 2005 : Singapore, Singapore) Conference location Singapore, Singapore Conference dates 6-11 Nov. 2005 Title of proceedings MM'05 : Proceedings of the 13th ACM International Conference on Multimedia Editor(s) [Unknown] Publication date 2005 Conference series ACM International Conference on Multimedia Start page 11 End page 20 Total pages 10 Publisher Association for Computing Machinery Place of publication New York, N. Y. Keyword(s) topic transition detection hierarchical Markov (semi-Markov) models coxian educational videos Summary In this paper we introduce a probabilistic framework to exploit hierarchy, structure sharing and duration information for topic transition detection in videos. Our probabilistic detection framework is a combination of a shot classification step and a detection phase using hierarchical probabilistic models. We consider two models in this paper: the extended Hierarchical Hidden Markov Model (HHMM) and the Coxian Switching Hidden semi-Markov Model (S-HSMM) because they allow the natural decomposition of semantics in videos, including shared structures, to be modeled directly, and thus enabling efficient inference and reducing the sample complexity in learning. Additionally, the S-HSMM allows the duration information to be incorporated, consequently the modeling of long-term dependencies in videos is enriched through both hierarchical and duration modeling. Furthermore, the use of the Coxian distribution in the S-HSMM makes it tractable to deal with long sequences in video. Our experimentation of the proposed framework on twelve educational and training videos shows that both models outperform the baseline cases (flat HMM and HSMM) and performances reported in earlier work in topic detection. The superior performance of the S-HSMM over the HHMM verifies our belief that duration information is an important factor in video content modeling. ISBN 1595930442 Language eng DOI 10.1145/1101149.1101153 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 ©2005, ACM Persistent URL http://hdl.handle.net/10536/DRO/DU:30044825 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   Connect to conference webpage 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 Tue, 01 May 2012, 11:00:41 EST Fri, 04 May 2012, 11:24:28 EST Fri, 04 May 2012, 11:25:48 EST Sat, 17 Jun 2017, 03:11:00 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 17 times in Scopus Search Google Scholar Access Statistics: 327 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 11:00:41 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.