.png){kind=icon}
返回
An improved GrabCut on multiscale features
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107292.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
the GrabCut can effectively extract the foreground according to features in a cartoon image; however, the performance is not so effective for a real image, because the feature extraction is independent of segmentation. To improve segmentation performance, this paper proposes an improved GrabCut which combines the segmentation and multiscale feature extraction into a unified model. In this model, the segmentation relies on multiscale features, and the multiscale features depend on multiscale decomposition. A novel total variation regularization is proposed in multiscale decomposition to preserve edges and remove the region inhomogeneity, by which the generalization of features for segmentation is improved. The features obtained by the multiscale decomposition are integrated into the segmentation process, and the foreground can be easily extracted from a proper scale. Experimental results indicate that, compared to the existing GrabCut and improved techniques, this method provides competitive performance in terms of the segmentation accuracy, while being insensitive to inhomogeneity. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Image segmentation
GrabCut
Multiscale feature
Total variation regularization
Inhomogeneity
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Voronoi region-based adaptive unsupervised color image segmentation基于Voronoi区域的自适应无监督彩色图像分割.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
查看更多内容