An adaptive and efficient unsupervised shot clustering algorithm for sports video

Liao, Jia, Wang, Guoren, Zhang, Bo, Zhou, Xiaofang and Yu, Ge (2007). An adaptive and efficient unsupervised shot clustering algorithm for sports video. In: , Advances in Database: Concepts, Systems and Applications. 12th International Conference on Database Systems for Advanced Applications (DASFAA 2007), Bangkok, Thailand, (127-139). 9-12 April 2007.


Author Liao, Jia
Wang, Guoren
Zhang, Bo
Zhou, Xiaofang
Yu, Ge
Title of paper An adaptive and efficient unsupervised shot clustering algorithm for sports video
Conference name 12th International Conference on Database Systems for Advanced Applications (DASFAA 2007)
Conference location Bangkok, Thailand
Conference dates 9-12 April 2007
Proceedings title Advances in Database: Concepts, Systems and Applications   Check publisher's open access policy
Journal name Advances in Databases: Concepts, Systems and Applications   Check publisher's open access policy
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2007
Sub-type Fully published paper
DOI 10.1007/978-3-540-71703-4_13
ISBN 978-3-540-71702-7
ISSN 0302-9743
1611-3349
Volume 4443
Start page 127
End page 139
Total pages 13
Language eng
Abstract/Summary Due to its tremendous commercial potential, sports video has become a popular research topic nowadays. As the bridge of low-level features and high-level semantic contents, automatic shot clustering is an important issue in the field of sports video content analysis. In previous work, many clustering approaches need some professional knowledge of videos, some experimental parameters, or some thresholds to obtain good clustering results. In this article, we present a new efficient shot clustering algorithm for sports video which is generic and does not need any prior domain knowledge. The novel algorithm, which is called Valid Dimension Clustering(VDC), performs in an unsupervised manner. For the high-dimensional feature vectors of video shots, a new dimensionality reduction approach is proposed first, which takes advantage of the available dimension histogram to get ”valid dimensions” as a good approximation of the intrinsic characteristics of data. Then the clustering algorithm performs on valid dimensions one by one to furthest utilize the intrinsic characteristics of each valid dimension. The iterations of merging and splitting of similar shots on each valid dimension are repeated until the novel stop criterion which is designed inheriting the theory of Fisher Discriminant Analysis is satisfied. At last, we apply our algorithm on real video data in our extensive experiments, the results show that VDC has excellent performance and outperforms other clustering algorithms.
Subjects 080604 Database Management
Keyword Sports video
Automatic shot clustering
Valid dimension clustering(VDC)
Q-Index Code E1
Additional Notes Proceedings published in 'Lecture Notes in Computer Science' Book series.

 
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