Thirdly this paper proposes a motion-based algorithm for represent frame extraction, which can extract represent frames dynamically according to the requirement of the user. It will extract more frames if there is much motion in a shot, and vice versa.
提出了一种基于运动和用户需求的代表帧抽取方法，可以根据用户的要求动态地抽取一定数量的代表帧，对运动较少的镜头抽取少量的代表帧，而对运动较大的镜头则抽取较多的代表帧，而且充分考虑到了代表帧在镜头中的分布情况。
参考来源 - 视频特征提取和视频镜头分析·2,447,543篇论文数据，部分数据来源于NoteExpress
情节代表帧选取方法是视频语义分析和基于内容的视频检索的很重要的方法。
Selecting episode representation frame is one of the important processes in video semantic analysis and content-based video retrieval.
该文在子镜头的关键帧提取方法基础上，利用模糊c -均值聚类算法，实现了一种基于子镜头聚类的情节代表帧选取方法。
An algorithm for selecting episode representation frames by using an approach of key frame extraction based on multiple characters and C-Mean fuzzy clustering is detailed in the paper.
为了得到这样一种组织，不仅要检测出镜头和情节这些视频单元的边界，还要提取镜头关键帧和选择情节有代表性的镜头和代表帧。
To achieve such an organization, it needs not only detect the boundary of shots and episodes, but also extract the key frames of shots and select the representative shots and frames for episodes.
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