Efficient margin-based query learning on action classification

In this paper, we propose a margin-based query learning algorithm for action recognition to reduce a laborious work on annotating action labels of time-series motion. The annotation is an inevitable task for designers of recognition systems with supervised learning techniques. Query learning is a kind of compensation approach for this, and can also be categorized into interactive learning. Our algorithm is a natural extension of maximum margin learning; a.k.a. support vector machines. Thanks to the theoretical analysis of the optimal condition of the maximum margin learning, the algorithm runs with a single and simple criterion. To prevent poor performance of the classifier learned with very few size of labeled motion data set, the algorithm exploits cluster information of massive unlabeled motion dataset. In contrast to the previous margin-based query learning methods, the algorithm has superiority in terms of stability. The empirical evaluation using real motion and synthetic dataset shows that our algorithm can achieve both drastic reduction of annotation cost and making robust classifiers.