.png){kind=icon}
返回
Genetic programming for evolving figure-ground segmentors from multiple features
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2016.07.055.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Figure-ground segmentation is a crucial preprocessing step for many image processing and computer vision tasks. Since different object classes need specific segmentation rules, the top-down approach, which learns from the object information, is more suitable to solve segmentation problems than the bottom-up approach. A problem faced by most existing top-down methods is that they require much human work/intervention, meanwhile introducing human bias. As genetic programming (GP) does not require users to specify the structure of solutions, we apply it to evolve segmentors that can conduct the figure-ground segmentation automatically and accurately. This paper aims to determine what kind of image information is necessary for GP to evolve capable segmentors (especially for images with high variations, e.g. varied object shapes or cluttered backgrounds). Therefore, seven different terminal sets are exploited to evolve segmentors, and images from four datasets (bitmap, Brodatz texture, Weizmann and Pascal databases), which are increasingly difficult for segmentation tasks, are selected for testing. Results show that the proposed GP based method can be successfully applied to diverse types of images. In addition, intensity based features are not sufficient for complex images, whereas features containing spectral and statistical information are necessary. Compared with four widely-used segmentation techniques, our method obtains consistently better segmentation performance. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Figure-ground segmentation
Genetic programming
Intensity based features
Gabor features
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:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
没有更多内容
没有更多内容