Efficient subsequence matching over large video databases

Zhou, Xiangmin, Zhou, Xiaofang, Chen, Lei and Bouguettaya, Athman (2012) Efficient subsequence matching over large video databases. The VLDB Journal, 21 4: 489-508. doi:10.1007/s00778-011-0255-5

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Author Zhou, Xiangmin
Zhou, Xiaofang
Chen, Lei
Bouguettaya, Athman
Title Efficient subsequence matching over large video databases
Journal name The VLDB Journal   Check publisher's open access policy
ISSN 1066-8888
Publication date 2012-08-01
Year available 2011
Sub-type Article (original research)
DOI 10.1007/s00778-011-0255-5
Open Access Status Not Open Access
Volume 21
Issue 4
Start page 489
End page 508
Total pages 20
Place of publication New York, NY, United States
Publisher Association for Computing Machinery
Language eng
Formatted abstract
Video similarity matching has broad applications such as copyright detection, news tracking and commercial monitoring, etc. Among these applications, one typical task is to detect the local similarity between two videos without the knowledge on positions and lengths of each matched subclip pair. However, most studies so far on video detection investigate the global similarity between two short clips using a pre-defined distance function. Although there are a few works on video subsequence detection, all these proposals fail to provide an effective query processing mechanism. In this paper, we first generalize the problem of video similarity matching. Then, a novel solution called consistent keyframe matching (CKM) is proposed to solve the problem of subsequence matching based on video segmentation. CKM is designed with two goals: (1) good scalability in terms of the query sequence length and the size of video database and (2) fast video subsequence matching in terms of processing time. Good scalability is achieved by employing a batch query paradigm, where keyframes sharing the same query space are summarized and ordered. As such, the redundancy of data access is eliminated, leading to much faster video query processing. Fast subsequence matching is achieved by comparing the keyframes of different video sequences. Specifically, a keyframe matching graph is first constructed and then divided into matched candidate subgraphs. We have evaluated our proposed approach over a very large real video database. Extensive experiments demonstrate the effectiveness and efficiency of our approach.
Keyword Consistent segments
Keyframe matching graph
Hypersurface partition
Batch query processing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 20 September 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 6 times in Scopus Article | Citations
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