体育视频挖掘与马赛克

Tao Mei, Yu-Fei Ma, He-Qin Zhou, Wei-Ying Ma, HongJiang Zhang
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引用次数: 21

摘要

视频是一种具有大量冗余的信息密集型媒体。因此,能够对视频数据进行结构或语义挖掘,以实现高效的浏览、总结和高亮提取是非常必要的。在本文中,我们提出了一种用于体育视频分析的关键事件和结构挖掘的通用方法。对每个镜头生成马赛克,作为镜头内容的代表图像。基于镶嵌的方法,对体育视频进行了有先验知识和无先验知识的挖掘。在没有先验知识的情况下,我们的系统可以通过区分那些没有基本内容的片段来定位游戏,比如休息。如果有先验知识,则使用从马赛克中提取的鲁棒特征来检测油气藏中的关键事件。实验结果证明了该方法的有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sports Video Mining with Mosaic
Video is an information-intensive media with much redundancy. Therefore, it is desirable to be able to mine structure or semantics of video data for efficient browsing, summarization and highlight extraction. In this paper, we propose a generic approach to key-event as well as structure mining for sports video analysis. Mosaic is generated for each shot as the representative image of shot content. Based on mosaic, sports video is mined by the method with prior knowledge and without prior knowledge. Without prior knowledge, our system may locate plays by discriminating those segments without essential content, such as breaks. If prior knowledge is available, the key-events in plays are detected using robust features extracted from mosaic. Experimental results have demonstrated the effectiveness and robustness of this sports video mining approach.
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