使用遮挡边界和时间相干超像素的交互式视频分割

Radu Dondera, Vlad I. Morariu, Yulu Wang, L. Davis
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引用次数: 15

摘要

提出了一种基于遮挡和长期时空结构线索的交互式视频分割系统。用户监督被纳入超像素图聚类框架,该框架与现有技术的关键区别在于,它根据遮挡边界检测器的输出修改图。使用较长的时间间隔(最多100帧)使我们的系统能够显著减少相对于当前系统状态的注释工作。尽管分割结果不太完美,但它们是有效的,可以用于视频的弱监督学习或视频内容描述。我们不依赖于区分对象外观模型,并允许同时提取多个前景对象,如果存在多个对象,则节省用户时间。基于遮挡边界的无监督聚类的其他实验证明了该线索对视频分割的重要性,从而验证了我们的系统设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interactive video segmentation using occlusion boundaries and temporally coherent superpixels
We propose an interactive video segmentation system built on the basis of occlusion and long term spatio-temporal structure cues. User supervision is incorporated in a superpixel graph clustering framework that differs crucially from prior art in that it modifies the graph according to the output of an occlusion boundary detector. Working with long temporal intervals (up to 100 frames) enables our system to significantly reduce annotation effort with respect to state of the art systems. Even though the segmentation results are less than perfect, they are obtained efficiently and can be used in weakly supervised learning from video or for video content description. We do not rely on a discriminative object appearance model and allow extracting multiple foreground objects together, saving user time if more than one object is present. Additional experiments with unsupervised clustering based on occlusion boundaries demonstrate the importance of this cue for video segmentation and thus validate our system design.
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