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Video-Story Composition via Plot Analysis

机译:视频故事构图通过绘图分析

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摘要

We address the problem of composing a story out of multiple short video clips taken by a person during an activity or experience. Inspired by plot analysis of written stories, our method generates a sequence of video clips ordered in such a way that it reflects plot dynamics and content coherency. That is, given a set of multiple video clips, our method composes a video which we call a video-story. We define metrics on scene dynamics and coherency by dense optical flow features and a patch matching algorithm. Using these metrics, we define an objective function for the video-story. To efficiently search for the best video-story, we introduce a novel Branch-and-Bound algorithm which guarantees the global optimum. We collect the dataset consisting of 23 video sets from the web, resulting in a total of 236 individual video clips. With the acquired dataset, we perform extensive user studies involving 30 human subjects by which the effectiveness of our approach is quantitatively and qualitatively verified.
机译:我们解决了在活动或经验期间由一个人拍摄的多个短视频剪辑中的故事的问题。灵感来自书面故事的绘图分析,我们的方法产生一系列的视频剪辑,以这样的方式,即它反映绘图动态和内容一致性。也就是说,给定一组多个视频剪辑,我们的方法撰写了一个我们称之为视频故事的视频。我们在茂密的光学流特征和补丁匹配算法上定义了场景动态和一致性的度量。使用这些指标,我们为视频故事定义了一个客观函数。为了有效地搜索最佳视频故事,我们介绍了一种新颖的分支和绑定算法,保证了全局最优的算法。我们收集由Web中的23个视频集组成的数据集,总共236个单独的视频剪辑。通过收购数据集,我们进行广泛的用户研究,涉及30个人类受试者,通过该主体,通过该主题是定量和定性地验证的方法的有效性。

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