Max-margin adaptive model for complex video pattern recognition

Yu, Litao, Shao, Jie, Xu, Xin-Shun and Shen, Heng Tao (2014) Max-margin adaptive model for complex video pattern recognition. Multimedia Tools And Applications, 74 2: 505-521. doi:10.1007/s11042-014-2010-6

Author Yu, Litao
Shao, Jie
Xu, Xin-Shun
Shen, Heng Tao
Title Max-margin adaptive model for complex video pattern recognition
Journal name Multimedia Tools And Applications   Check publisher's open access policy
ISSN 1380-7501
Publication date 2014-05-10
Year available 2014
Sub-type Article (original research)
DOI 10.1007/s11042-014-2010-6
Open Access Status Not Open Access
Volume 74
Issue 2
Start page 505
End page 521
Total pages 17
Place of publication New York, United States
Publisher Springer
Language eng
Formatted abstract
Patternrecognitionmodels are usually used in a variety of applications ranging from video concept annotation to event detection. In this paper we propose a new framework called the max-margin adaptive (MMA) model for complex video pattern recognition, which can utilize a large number of unlabeled videos to assist the model training. The MMA model considers the data distribution consistence between labeled training videos and unlabeled auxiliary ones from the statistical perspective by learning an optimal mapping function which also broadens the margin between positive labeled videos and negative labeled videos to improve the robustness of the model. The experiments are conducted on two public datasets including CCV for video object/event detection and HMDB for action recognition. Our results demonstrate that the proposed MMA model is very effective on complex video pattern recognition tasks, and outperforms the state-of-the-art algorithms.
Keyword Video pattern recognition
Max-margin adaptive model
Event detection
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 10 May 2014.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
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Created: Fri, 16 May 2014, 19:16:01 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering