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dc.contributor.authorPhung, Dinh
dc.contributor.authorVenkatesh, Svetha
dc.contributor.authorDuong, Thi
dc.contributor.authorBui, Hung H.
dc.contributor.editorACM Press
dc.identifier.citationPhung, Dinh and Venkatesh, Svetha and Duong, Thi and Bui, Hung. 2005. Topic transition detection using hierarchical hidden Markov and semi-Markov models, in ACM Press (ed), 13th ACM International Conference on Multimedia (ACM 2005), Nov 6 2005, pp. 11-20. Singapore: Association for Computing Machinery (ACM).

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 ecient 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 (at HMM and HSMM) and performances reported in earlier work in topic detection. The superior performance of the S-HSMM over theHHMM veries our belief that duration information is an important factor in video content modeling.

dc.publisherACM Press
dc.subjectHierarchical Markov (Semi-Markov) Models
dc.subjectTopic Transition Detection
dc.subjectContent Analysis and Indexing
dc.subjectEducational Videos
dc.subjectInformation Storage and Retrieval
dc.titleTopic transition detection using hierarchical hidden Markov and semi-Markov models
dc.typeConference Paper
dcterms.source.titleProceedings of the 13th ACM International Conference on Multimedia
dcterms.source.seriesProceedings of the 13th ACM International Conference on Multimedia
dcterms.source.conference13th ACM International Conference on Multimedia (ACM 2005)
dcterms.source.conference-start-date6 Nov 2005
dcterms.source.placeNew York, USA

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curtin.accessStatusFulltext not available
curtin.facultySchool of Electrical Engineering and Computing
curtin.facultyDepartment of Computing
curtin.facultyFaculty of Science and Engineering

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