PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Transductive Transfer Learning for Action Recognition in Tennis Games
Nazli Faraji Davar, Teo de Campos, Josef Kittler and Fei Yan
In: 3rd International Workshop on Video Event Categorization, Tagging and Retrieval for Real-World Applications (VECTaR2011), 13 Nov 2011, Barcelona, Spain.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:8335
Deposited By:Teo de Campos
Deposited On:27 October 2011