Max-margin adaptive model for complex video pattern recognition

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