Max-margin adaptive model for complex video pattern recognition

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• 作者：Litao Yu (1)
  Jie Shao (2)
  Xin-Shun Xu (3)
  Heng Tao Shen (1)
  
  1. School of Information Technology and Electrical Engineering ; The University of Queensland ; Brisbane ; QLD ; 4072 ; Australia
  2. Department of Computer Science ; National University of Singapore ; Singapore ; 117417 ; Singapore
  3. School of Computer Science and Technology ; Shandong University ; Jinan ; 250101 ; Shandong ; People鈥檚 Republic of China
  
• 关键词：Video pattern recognition ; Max ; margin adaptive model ; Event detection
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：74
• 期：2
• 页码：505-521
• 全文大小：1,613 KB
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• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

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.