When accessing contents in ever-increasing multimedia chunks, indexing and analysis of video data are key steps. Among different types of videos, sports video is an important type of video and it is under research focus now. Due to the increasing demands from audience, highlights extraction become meaningful. This paper proposed a mean shift clustering based semi-automatic sports video highlight annotation method. Specifically, given small pieces of annotated highlights, by adopting Mean Shift clustering and earth mover's distance (EMD), mid-level features of highlight shots are extracted and utilized to annotate other highlights automatically. There are 3 steps in the proposed method: First, extract signature of different features - Camera Motion Signature (CMS) for motion and Pivot Frame Signature (PFS) for color. Second, Camera motion's co-occurrence value is defined as Camera Motion Devotion Value (CMDV) and calculated as EMD distance between signatures. Decisive motion feature for highlights' occurrences is thus semi-automatically detected. Finally highlights are annotated based on these motion parameters and refined by color-based results. Another innovation of this paper is to combine semantic information with low-level feature aiding highlight annotation. Based on Highlight shot feature (HSF), we performed hierarchical highlight annotation and got promising results. Our method is tested on four video sequences comprising of different types of sports games including diving, swimming, and basketball, over 50,000 frames and experimental results demonstrate the effectiveness of our method.
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