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A Novel Sub-Shot Segmentation Method for User-generated Video

机译:一种新的用户生成视频子片段分割方法

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With the proliferation of the user-generated videos, temporal segmentation is becoming a challengeable problem. Traditional video temporal segmentation methods like shot detection are not able to work on unedited user-generated videos, since they often only contain one single long shot. We propose a novel temporal segmentation framework for user-generated video. It finds similar frames with a tree partitioning min-Hash technique, constructs sparse temporal constrained affinity sub-graphs, and finally divides the video into sub-shot-level segments with a dense-neighbor-based clustering method. Experimental results show that our approach outperforms all the other related works. Furthermore, it is indicated that the proposed approach is able to segment user-generated videos at an average human level.
机译:随着用户生成的视频的扩散,时间分割正在成为一个有挑战性的问题。传统的视频时间分段方法,如镜头检测,无法在未编辑的用户生成的视频上工作,因为它们通常只包含一个单长镜头。我们为用户生成的视频提出了一种新的时间分段框架。它发现具有树分区MIN-哈希技术的类似帧,构造稀疏的时间受限的亲和子图,并且最后将视频划分为具有密集邻的聚类方法的子拍级段。实验结果表明,我们的方法优于所有其他相关工程。此外,结果表明,所提出的方法能够在平均人类水平下划分用户生成的视频。

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