Improving classification of video shots using information-theoretic co-clustering

Peng Wang; Rui Cai; Shi Qiang Yang

doi:10.1109/ISCAS.2005.1464750

Improving classification of video shots using information-theoretic co-clustering

Peng Wang, Rui Cai, Shi Qiang Yang

Research output: Contribution to journal › Conference article › peer-review

Abstract

Automatic categorization of video shots is very useful in applications of video content analysis and retrieval, such as structure parsing and semantic event recognition. In order to consider the relationships between different video features and provide more accurate similarity measure for video shot classification, in this paper, informationtheoretic co-clustering is utilized to group the video shots and features simultaneously. In addition, Bayesian Information Criterion is employed to automatically estimate the number of clusters for both the video shots and features. Evaluation on 1374 shots extracted from around 4-hour sports videos shows very encouraging results.

Original language	English
Article number	1464750
Pages (from-to)	964-967
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
DOIs	https://doi.org/10.1109/ISCAS.2005.1464750
State	Published - 2005
Externally published	Yes
Event	IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan Duration: 23 May 2005 → 26 May 2005

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10.1109/ISCAS.2005.1464750

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title = "Improving classification of video shots using information-theoretic co-clustering",

abstract = "Automatic categorization of video shots is very useful in applications of video content analysis and retrieval, such as structure parsing and semantic event recognition. In order to consider the relationships between different video features and provide more accurate similarity measure for video shot classification, in this paper, informationtheoretic co-clustering is utilized to group the video shots and features simultaneously. In addition, Bayesian Information Criterion is employed to automatically estimate the number of clusters for both the video shots and features. Evaluation on 1374 shots extracted from around 4-hour sports videos shows very encouraging results.",

author = "Peng Wang and Rui Cai and Yang, {Shi Qiang}",

year = "2005",

doi = "10.1109/ISCAS.2005.1464750",

language = "English",

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journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 ; Conference date: 23-05-2005 Through 26-05-2005",

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AU - Wang, Peng

AU - Cai, Rui

AU - Yang, Shi Qiang

PY - 2005

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AB - Automatic categorization of video shots is very useful in applications of video content analysis and retrieval, such as structure parsing and semantic event recognition. In order to consider the relationships between different video features and provide more accurate similarity measure for video shot classification, in this paper, informationtheoretic co-clustering is utilized to group the video shots and features simultaneously. In addition, Bayesian Information Criterion is employed to automatically estimate the number of clusters for both the video shots and features. Evaluation on 1374 shots extracted from around 4-hour sports videos shows very encouraging results.

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U2 - 10.1109/ISCAS.2005.1464750

DO - 10.1109/ISCAS.2005.1464750

M3 - Conference article

AN - SCOPUS:41149106223

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EP - 967

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

M1 - 1464750

T2 - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005

Y2 - 23 May 2005 through 26 May 2005

ER -

Improving classification of video shots using information-theoretic co-clustering

Abstract

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