A semi-supervised incremental learning framework for sports video view classification

Sports videos have special characteristics such as well-defined video structure, specialized sports syntax, and typically having some canonical view types. In this paper, we propose a semi-supervised incremental learning framework for sports video view classification. Baseball is selected as an example to explain the main ideas. In order to obtain an optimal model based on a small number of pre-labeled training samples, the semi-supervised incremental learning framework explores the local distributed properties of the video sequences and sufficiently utilizes the information of a positive model pool and a negative model pool. After each round of online optimization process for the under-investigating video, a locally-optimized positive model and a set of negative models are added into the positive model pool and the negative model pool according to some heuristic criteria, respectively. Experiments results on real sports video data show that the proposed system is effective and promising