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

Peng Wang, Rui Cai, Shi Qiang Yang

Research output: Contribution to journalConference articlepeer-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 languageEnglish
Article number1464750
Pages (from-to)964-967
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
DOIs
StatePublished - 2005
Externally publishedYes
EventIEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan
Duration: 23 May 200526 May 2005

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