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...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
ACM Press
2005
|
| Subjects: | |
| Online Access: | http://doi.acm.org/10.1145/1101149.1101153 http://hdl.handle.net/20.500.11937/18410 |
| _version_ | 1848749736891252736 |
|---|---|
| author | Phung, Dinh Venkatesh, Svetha Duong, Thi Bui, Hung H. |
| author2 | ACM Press |
| author_facet | ACM Press Phung, Dinh Venkatesh, Svetha Duong, Thi Bui, Hung H. |
| author_sort | Phung, Dinh |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:25:41Z |
| format | Conference Paper |
| id | curtin-20.500.11937-18410 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:25:41Z |
| publishDate | 2005 |
| publisher | ACM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-184102019-02-19T05:34:59Z Topic transition detection using hierarchical hidden Markov and semi-Markov models Phung, Dinh Venkatesh, Svetha Duong, Thi Bui, Hung H. ACM Press Hierarchical Markov (Semi-Markov) Models Topic Transition Detection Management Coxian Algorithms Content Analysis and Indexing Educational Videos Information Storage and Retrieval 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. 2005 Conference Paper http://hdl.handle.net/20.500.11937/18410 http://doi.acm.org/10.1145/1101149.1101153 ACM Press restricted |
| spellingShingle | Hierarchical Markov (Semi-Markov) Models Topic Transition Detection Management Coxian Algorithms Content Analysis and Indexing Educational Videos Information Storage and Retrieval Phung, Dinh Venkatesh, Svetha Duong, Thi Bui, Hung H. Topic transition detection using hierarchical hidden Markov and semi-Markov models |
| title | Topic transition detection using hierarchical hidden Markov and semi-Markov models |
| title_full | Topic transition detection using hierarchical hidden Markov and semi-Markov models |
| title_fullStr | Topic transition detection using hierarchical hidden Markov and semi-Markov models |
| title_full_unstemmed | Topic transition detection using hierarchical hidden Markov and semi-Markov models |
| title_short | Topic transition detection using hierarchical hidden Markov and semi-Markov models |
| title_sort | topic transition detection using hierarchical hidden markov and semi-markov models |
| topic | Hierarchical Markov (Semi-Markov) Models Topic Transition Detection Management Coxian Algorithms Content Analysis and Indexing Educational Videos Information Storage and Retrieval |
| url | http://doi.acm.org/10.1145/1101149.1101153 http://hdl.handle.net/20.500.11937/18410 |