Unsupervised sports video scene clustering and its applications to story units detection

Abstract

In this paper, we present a new and efficient clustering approach for scene analysis in sports video. This method is generic and does not require any prior domain knowledge. It performs in an unsupervised manner and relies on the scene likeness analysis of the shots in the video. The two most similar shots are merged into the same scene in each iteration. And this procedure is repeated until the merging stop criterion is satisfied. The stop criterion is defined based on a <i>J value </i>which is defined according to the Fisher Discriminant Function. We call this method <i>J-based Scene Clustering</i>. By using this method, the low-level video content representation-shots could be clustered into the midlevel video content representation-scenes, which are useful for high-level sports video content analysis such as playbreak parsing, story units detection, highlights extraction and summarization, etc. Experimental results obtained from various types of broadcast sports videos demonstrate the efficacy of the proposed approach. Moreover, in this paper, we also present a simple application of our scene clustering method to story units detection in periodic sports videos like archery video, diving video and so on. The experimental results are encouraging.