Multimedia Tools and Applications

, Volume 74, Issue 2, pp 505–521 | Cite as

Max-margin adaptive model for complex video pattern recognition

Article

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.

Keywords

Video pattern recognition Max-margin adaptive model Event detection 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Litao Yu
    • 1
  • Jie Shao
    • 2
  • Xin-Shun Xu
    • 3
  • Heng Tao Shen
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
  2. 2.Department of Computer ScienceNational University of SingaporeSingaporeSingapore
  3. 3.School of Computer Science and TechnologyShandong UniversityJinanPeople’s Republic of China

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