Shot Type Classification in Sports Video Based on Visual Attention

In this paper, we present a new method for classifying shot type in sports video based on visual attention. The problem is important for applications such as video structure analysis and content understanding. In particular, two-stage off-line learning processes perform knowledge extraction of semantic concepts and automatic shot classification, respectively. In the first stage, the extracted prominent regions are used as a good pattern in semantic concept level. Then a number of global features are defined as efficient input of the shot type classifier in the second stage. The identification of semantic concepts and classification of shot are based on human visual system. Hence, this framework can adequately capture the uncertainty or ambiguity of scales of a shot. Experimental results show the excellent performance of the approach.