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Topic transition detection using hierarchical hidden Markov and semi-Markov models

12 years 7 months ago
Topic transition detection using hierarchical hidden Markov and semi-Markov models
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 experimen...
Dinh Q. Phung, Thi V. Duong, Svetha Venkatesh, Hun
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where MM
Authors Dinh Q. Phung, Thi V. Duong, Svetha Venkatesh, Hung Hai Bui
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