Semantic Video Shot Segmentation Based on Color Ratio Feature and SVM

With the fast development of video semantic analysis, there has been increasing attention to the typical issue of the semantic analysis of soccer program. Based on the color feature analysis, this paper focuses on the video shot segmentation problem from the perspective of semantic analysis, i.e. the semantic shot segmentation. Most existing works segment and classify the shot by using the dominant color of the field in soccer video. In this paper, we extend the traditional dominant color to several semantic colors, and define the color ratio feature. Then, the support vector machine is used for shot classification. The experimental result shows that the color ratio feature is useful to improve the performance. Furthermore, considering the temporal variations in the semantic color due to environment, an adaptive semantic color extraction algorithm is proposed, and the influence of the number of semantic colors on classification precision is evaluated also.