Medical Video Event Classification Using Shared Features

Advances in video technology are being incorporated into todaypsilas medical research and education. Medical videos contain important medical events, such as diagnostic or therapeutic operations. Automatic discovery and classification of these events are highly desirable and very useful. In this paper, we present a novel method for multi-class educational medical video event categorization. Our method employs a learning procedure based on boosted decision stumps. There are two key contributions in this paper. The first contribution is that the proposed multi-class boosting algorithms utilize the common features which can be shared among different video event categories. Compared with the class-specific features, the entire set of shared features can provide more efficient and reliable representation to classify multiple video event categories. The second key contribution of this paper is the adaption of the spacetime interest point detection techniques for feature extraction on both the spatial dimension and the temporal dimension. Experimental results have shown that the proposed approach is a very promising strategy for solving the multi-class video event classification problem.