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Key Object-based Static Video Summarization

机译:基于关键对象的静态视频汇总

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In this paper, we present a system for object-based video summarization facilitated by an efficient video object segmentation system. We eliminate the redundancy not only from spatial and temporal domain, but also from content domain. First, we detect shot boundaries and extract video objects by a 3D graph-based algorithm. Once the objects are obtained, the shape of the objects need to be represented. The key objects are extracted in a global manner by K-means clustering of shapes. Experimental results on the proposed object-based scheme combined with efficient video object segmentation show desirable summarization.
机译:在本文中,我们介绍了一种由有效的视频对象分段系统促进了基于对象的视频摘要系统。我们不仅从空间和时间域中消除冗余,还消除了来自内容域的冗余。首先,我们通过基于3D图形的算法检测拍摄边界并提取视频对象。一旦获得了对象,需要表示对象的形状。通过k-means群集以全局方式提取关键对象。基于对象的基于对象的方案的实验结果结合有效的视频对象分割显示了理想的概括。

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