Semantic Video Content Structuring and Visualization

Abstract

In this paper, we propose a novel approach to visualize video content based on shot description of motion activity and textual information of closed caption in MPEG-2 sports videos. In order to speed up in scene change detection, GOP-based approach first checks video streams GOP by GOP and then finds out the actual scene boundaries in the frame level. Segmented shots are described by the proposed object-based motion activity descriptor. The descriptor is computed based on the object 2D-histogram, in which long-term consistency of spatial-temporal relationship of moving objects within video shots is considered. Utilizing the characterized features of motion activity in video shots, video clips are recognized by the proposed algorithm of shot identification. Subsequently, the specific shots of interest are selected and the proposed mechanism of closed caption localization is exploited to detect captions in these shots. Moreover, the SOM (Self-Organization Map) based algorithm is designed as a filter to distinguish the superimposed closed captions from the high-textured background regions. Finally, we construct a sports video content visualization system and provide the table of video content composed of the hierarchical structure of story unit, consecutive shots and closed captions. Furthermore, summarization mechanism – the dynamic tree structure of video content is provided. The experimental results show the effectiveness of the proposed system and reveal the feasibility of the hierarchical structuring of video content.