Motion pattern based video classification using support vector machines

Semantic classification is an effective approach to the management of vast digital video data. In this paper, we propose a new semantic classification scheme based on motion patterns. With such scheme, the motion patterns in video clips can be mapped to semantic conceptions effectively. In our implementation, Motion Texture [1] is employed as motion pattern descriptor, which can be extracted from shots or video clips. By using kernel support vector machines (SVMs), we have devised an optimized multi-class classifier to link low level features with conceptions. Experimental results indicate that our approach is an effective solution for motion pattern based semantic video classification.