Robust automatic video object segmentation with graphcut assisted by SURF features
Robust automatic video object segmentation with graphcut assisted by SURF features
复制标题
SURF 功能辅助下的鲁棒自动视频对象分割和图形切割
DOI：
10.1109/icip.2012.6466854
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Toshinori Watanabe
中科院分区：
文献类型：
--
作者：
Satomi Kudo;Hisashi Koga;Takanori Yokoyama;Toshinori Watanabe
视频对象分割是区分视频中的前景和背景的任务。以往基于图割分割的视频对象自动分割方法大多采用运动和颜色线索来分割前景和背景。因此，当运动和/或颜色变得无序时，分割结果劣化，这通常发生在运动对象停止时和灯打开/关闭时。提出了一种新的对不稳定运动和颜色具有鲁棒性的视频自动分割方法。为了实现鲁棒性，graphcut分割由SURF特征支持，SURF特征对尺度、旋转和亮度的变化具有高度不变性。特别是，我们的方法匹配的SURF功能之间的两个连续的帧和修改的分割结果时，匹配的SURF功能被分配不同的标签。
Video object segmentation is a task to distinguish the foreground from the background in videos. Most previous research on automatic video object segmentation based on graphcut segmentation uses the motion cue and the color cue to separate the background from the foreground. Consequently, the segmentation result deteriorates when the motion and/or the color becomes disordered, which typically occurs when a moving object stops and when a light is switched on/off. This paper proposes a new automatic video segmentation method robust to unstable motion and color. To achieve robustness, the graphcut segmentation is supported by the SURF feature, which is highly invariant to the change of scale, rotation, and luminance. In particular, our method matches the SURF features between two consecutive frames and modifies the segmentation result when the matched SURF features are assigned different labels.