Automatic annotation of court games with structured output learning

We investigate the application of structured output learning (SOL) in automatic annotation of court games. We formulate the problem of event classification in court games as one of learning a mapping from features to structured labels, and employ structured SVM to achieve a max-margin solution. We compare closely the more popular generative approach based on the hidden Markov model (HMM) with our discriminative approach on both artificial games and two real world tennis games, and demonstrate the advantage of our method.