Adaptive subspace symbolization for content-based video detection

Zhou, Xiangmin, Zhou, Xiaofang, Chen, Lei, Shu, Yanfeng, Bouguettaya, Athman and Taylor, John A. (2010) Adaptive subspace symbolization for content-based video detection. IEEE Transactions on Knowledge and Data Engineering, 22 10: 1372-1387.

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Author Zhou, Xiangmin
Zhou, Xiaofang
Chen, Lei
Shu, Yanfeng
Bouguettaya, Athman
Taylor, John A.
Title Adaptive subspace symbolization for content-based video detection
Journal name IEEE Transactions on Knowledge and Data Engineering  (ERA 2012 Listed)    (ERA 2010 Rank A)   Check publisher's open access policy
Publication date 2010-10
Sub-type Article
Year available 2009
DOI 10.1109/TKDE.2009.171
Volume number 22
Issue number 10
ISSN 1041-4347; 1558-2191
Start page 1372
End page 1387
Total pages 16
Place of publication Piscataway, NJ, United States
Publisher IEEE
Collection year 2011
Language eng
Abstract Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to extract a series of dominant subspaces of variable lengths from the whole visual feature space without the constraint of dimension consecutiveness. A stable visual dictionary is built by clustering the video keyframes over each dominant subspace. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a series of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video retrieval. The experimental results demonstrate the high effectiveness and efficiency of SUDS.
Keyword Video detection
Subspace symbolization
Variable length partition
Query optimization
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Available online July 2009

Document type: Journal Article
Sub-type: Article
Collections: Official 2011 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Sun, 05 Sep 2010, 00:06:06 EST