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A discrete bio-inspired metaheuristic algorithm for efficient and accurate image matting
Memetic Computing ( IF 3.3 ) Pub Date : 2018-09-05 , DOI: 10.1007/s12293-018-0275-4
Zhao-Quan Cai , Liang Lv , Han Huang , Yi-Hui Liang

With the development of digital multimedia technologies, image matting has become one of the most popular research problem in academic field and been widely applied in industrial communities. The key challenge of image matting is how to extract the foreground region (target region) from a given image accurately. Sampling-based image matting technology implements matting by sampling some foreground pixels and background pixels from known regions and finding the best foreground–background sample pair for every undetermined pixel. The best foreground–background sample pair represents the true foreground and background colors of the corresponding undetermined pixel and they can estimate the region of this undetermined pixel accurately. Therefore, the quality of matting depends on whether the best sample pair can be found. This search process can be regarded as a combinational optimization problem. In this paper, in order to obtain more accurate matting result, we applied a bio-inspired metaheuristic algorithm to solve this problem, which is based on the promising earthworm optimization algorithm (EWA). By analyzing the property of this optimization problem, we upgrade two reproductions and the cauchy mutation of EWA to discrete calculations. The proposed algorithm is called as the discrete earthworm optimization algorithm (D-EWA). By comparing with existing optimization algorithms on a standard benchmark dataset, the experimental results show that the proposed D-EWA can obtain more accurate matting results on both visual effect and quantitative metric.

中文翻译:

一种离散的启发式元启发式算法,可实现高效,准确的图像消光

随着数字多媒体技术的发展,图像消光已成为学术界最受欢迎的研究问题之一,并已广泛应用于工业界。图像消光的关键挑战是如何从给定图像中准确提取前景区域(目标区域)。基于采样的图像抠像技术可通过对已知区域中的一些前景像素和背景像素进行采样,并为每个未确定的像素找到最佳的前景-背景样本对来实现抠图。最佳前景-背景样本对代表相应未确定像素的真实前景色和背景色,它们可以准确估计该未确定像素的区域。因此,消光的质量取决于能否找到最佳样品对。该搜索过程可以视为组合优化问题。为了获得更准确的消光效果,我们基于有前景的earth优化算法(EWA),应用了一种仿生的启发式元启发式算法来解决此问题。通过分析此优化问题的性质,我们将两次复制和EWA的柯西突变升级为离散计算。该算法被称为离散earth优化算法(D-EWA)。通过与标准基准数据集上的现有优化算法进行比较,实验结果表明,提出的D-EWA可以在视觉效果和定量指标上获得更准确的消光效果。我们基于有前景的earth优化算法(EWA),应用了生物启发式元启发式算法来解决此问题。通过分析此优化问题的性质,我们将两次复制和EWA的柯西突变升级为离散计算。该算法被称为离散earth优化算法(D-EWA)。通过与标准基准数据集上的现有优化算法进行比较,实验结果表明,提出的D-EWA可以在视觉效果和定量指标上获得更准确的消光效果。我们基于有前景的earth优化算法(EWA),应用了生物启发式元启发式算法来解决此问题。通过分析此优化问题的性质,我们将两次复制和EWA的柯西突变升级为离散计算。该算法被称为离散earth优化算法(D-EWA)。通过与标准基准数据集上的现有优化算法进行比较,实验结果表明,提出的D-EWA可以在视觉效果和定量指标上获得更准确的消光效果。我们将两个复制品和EWA的柯西突变升级为离散计算。该算法被称为离散earth优化算法(D-EWA)。通过与标准基准数据集上的现有优化算法进行比较,实验结果表明,提出的D-EWA可以在视觉效果和定量指标上获得更准确的消光效果。我们将两个复制品和EWA的柯西突变升级为离散计算。该算法被称为离散earth优化算法(D-EWA)。通过与标准基准数据集上的现有优化算法进行比较,实验结果表明,提出的D-EWA可以在视觉效果和定量指标上获得更准确的消光效果。
更新日期:2018-09-05
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