Image matting is an essential image processing technology due to its wide range of applications. Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs. It is essentially a large-scale multi-peak optimization problem of pixel pairs. Previous study shows that particle swarm optimization (PSO) can effectively optimize the pixel pairs. However, it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima. To address this problem, this work presents a parameter-free strategy for PSO called adaptive convergence speed controller (ACSC). ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator (CPPRO) and pixel pair reset operator (PPRO) during the iteration. ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution. PPRO is performed to avoid premature convergence when the alpha mattes regarding two selected particles are highly similar. Experimental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.

中文翻译：

---

PSO-ACSC：用于图像抠像的大规模进化算法

由于其广泛的应用，图像消光是必不可少的图像处理技术。基于采样的图像遮罩是图像遮罩研究的主要分支之一，该研究通过选择最佳像素对来估计alpha遮罩。从本质上讲，这是像素对的大规模多峰优化问题。先前的研究表明，粒子群优化（PSO）可以有效地优化像素对。然而，它仍然遭受过早的收敛问题，该问题经常发生在涉及大量局部最优的像素对优化中。为了解决这个问题，这项工作提出了一种用于PSO的无参数策略，称为自适应收敛速度控制器（ACSC）。在迭代过程中，ACSC通过竞争性像素对重组算子（CPPRO）和像素对重置算子（PPRO）监视并有条件地控制粒子。当大多数像素对的性能都比最好的解决方案差时，ACSC会执行CPPRO来提高颗粒的竞争力。当有关两个选定粒子的Alpha遮罩高度相似时，执行PPRO以避免过早收敛。实验结果表明，与最新的进化算法相比，ACSC显着提高了PSO的图像抠像性能，并提供了具有竞争力的alpha遮罩。当有关两个选定粒子的Alpha遮罩高度相似时，执行PPRO以避免过早收敛。实验结果表明，与最新的进化算法相比，ACSC显着提高了PSO的图像抠像性能，并提供了具有竞争力的alpha遮罩。当有关两个选定粒子的Alpha遮罩高度相似时，执行PPRO以避免过早收敛。实验结果表明，与最新的进化算法相比，ACSC显着提高了PSO的图像抠像性能，并提供了具有竞争力的alpha遮罩。