Improving sampling-based image matting with cooperative coevolution differential evolution algorithm
Improving sampling-based image matting with cooperative coevolution differential evolution algorithm
复制标题
使用协作协同进化差分进化算法改进基于采样的图像抠图
DOI：
10.1007/s00500-016-2250-7
复制
发表时间：
2017-08-01
期刊：
影响因子：
4.1
通讯作者：
Liang, Yi-Hui
中科院分区：
文献类型：
--
作者：
Cai, Zhao-Quan;Lv, Liang;Liang, Yi-Hui
图像抠图是图像编辑中的一项基本操作，对视频制作有着重要的影响。本文探索了基于采样的图像抠图技术，旨在提高抠图结果的精度。基于采样的图像抠图技术的结果取决于所选择的样本。每个待定像素都需要前景和背景像素来估计待定像素是否在图像的前景区域。这些前景像素和背景像素从已知区域中采样，形成样本对。高质量的样品对可以提高抠图结果的准确性。因此，如何对所有待定像素寻找最佳的样本对是基于采样的图像抠图技术的关键优化问题，称为“样本优化问题”。为了提高搜索高质量样本对的效率，本文提出了一种协同进化差分进化（DE）算法来解决这一优化问题。将强相关像素分成一组，协同搜索最佳样本对。为了避免遗传算法的过早收敛，采用分散策略来保持种群的多样性。除了。提出了一个简单而有效的评价函数来区分各种候选解的质量。采用现有的优化方法、原始DE算法和一种流行的进化算法进行比较。实验结果表明，所提出的协同进化DE算法能够搜索到更高质量的样本对，提高了基于采样的图像抠图的精度。
Image matting is a fundamental operator in image editing and has significant influence on video production. This paper explores sampling-based image matting technology, with the aim to improve the accuracy of matting result. The result of sampling-based image matting technology is determined by the selected samples. Every undetermined pixel needs both a foreground and background pixel to estimate whether the undetermined one is in the foreground region of the image. These foreground pixels and background pixels are sampled from known regions, which form sample pairs. High-quality sample pairs can improve the accuracy of matting results. Therefore, how to search for the best sample pairs for all undetermined pixels is a key optimization problem of sampling-based image matting technology, termed "sample optimization problem." In this paper, in order to improve the efficiency of searching for high-quality sample pairs, we propose a cooperative coevolution differential evolution (DE) algorithm in solution to this optimization problem. Strong-correlate pixels are divided into a group to cooperatively search for the best sample pairs. In order to avoid premature convergence of DE algorithm, a scattered strategy is used to keep the diversit) of population. Besides. a simple but effective evaluation function is proposed to distinguish the quality of various candidate solutions. The existing optimization method, original DE algorithm and a popular evolution algorithm are used for comparison. The experimental results demonstrate that the proposed cooperative coevolution DE algorithm can search for higher-quality sample pairs and improve the accuracy of sampling-based image matting.