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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.

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Title Topic transition detection using hierarchical hidden Markov and semi-Markov models
Author(s) Phung, Dinh Q.ORCID iD for Phung, Dinh Q. orcid.org/0000-0002-9977-8247
Duong, T. V.
Venkatesh, S.ORCID iD for 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
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