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Video-Story Composition via Plot Analysis

机译:通过情节分析制作视频故事

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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:我们解决了由一个人在活动或体验中拍摄的多个短视频片段中组成一个故事的问题。受书面故事情节分析的启发,我们的方法生成了一系列视频片段,其顺序以反映情节动态和内容连贯性的方式排列。也就是说,给定一组多个视频剪辑,我们的方法将组成一个视频,我们称之为视频故事。我们通过密集的光流特征和斑块匹配算法来定义场景动态和相干性的度量。使用这些指标,我们为视频故事定义了一个目标函数。为了有效地搜索最佳视频故事,我们引入了一种新颖的“分支限界”算法,该算法可以保证全局最优。我们从网络上收集了由23个视频集组成的数据集,总共产生了236个单独的视频剪辑。利用所获取的数据集,我们进行了涉及30个人类受试者的广泛用户研究,由此定量和定性地验证了我们方法的有效性。

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