Transductive transfer learning for action recognition in tennis games

This paper investigates the application of transductive transfer learning methods for action classification. The application scenario is that of off-line video annotation for retrieval. We show that if a classification system can analyze the unlabeled test data in order to adapt its models, a significant performance improvement can be achieved. We applied it for action classification in tennis games for train and test videos of different nature. Actions are described using HOG3D features and for transfer we used a method based on feature re-weighting and a novel method based on feature translation and scaling.