论文标题
使用自适应粒子群优化和模糊c均值的颜色图像分割
Color Image Segmentation using Adaptive Particle Swarm Optimization and Fuzzy C-means
论文作者
论文摘要
分割将图像分配到包含具有相似属性的像素的不同区域。模糊C均值聚类算法（FCM）的标准非上下文变体，考虑到其简单性通常用于图像分割。使用FCM的缺点就像它取决于簇数量的初始猜测和对噪声高度敏感的猜测。无法使用FCM获得令人满意的视觉片段。粒子群优化（PSO）属于进化算法的类别，与遗传算法（气体）相比，收敛速度良好，参数较少。 PSO的优化版本可以与FCM结合使用，以作为算法的适当初始化器，从而降低其对初始猜测的敏感性。一种称为自适应颗粒群优化（APSO）的混合PSO算法，该算法使用群体行为的见解，改善了各种超级参数，例如惯性重量，学习因素，超过标准PSO的学习因素，从而可以改善群集质量。本文提出了一种新的图像分割算法，称为自适应粒子群优化和模糊C均值聚类算法（APSOF），该算法基于自适应粒子群优化（APSO）和模糊的C-Means群集。实验结果表明，APSOF算法在正确识别最佳群集中心时具有优于FCM，并通过导致对图像像素的准确分类。因此，与经典的粒子群优化（PSO）和模糊C均值聚类算法（FCM）相比，APSOF算法具有出色的性能。
Segmentation partitions an image into different regions containing pixels with similar attributes. A standard non-contextual variant of Fuzzy C-means clustering algorithm (FCM), considering its simplicity is generally used in image segmentation. Using FCM has its disadvantages like it is dependent on the initial guess of the number of clusters and highly sensitive to noise. Satisfactory visual segments cannot be obtained using FCM. Particle Swarm Optimization (PSO) belongs to the class of evolutionary algorithms and has good convergence speed and fewer parameters compared to Genetic Algorithms (GAs). An optimized version of PSO can be combined with FCM to act as a proper initializer for the algorithm thereby reducing its sensitivity to initial guess. A hybrid PSO algorithm named Adaptive Particle Swarm Optimization (APSO) which improves in the calculation of various hyper parameters like inertia weight, learning factors over standard PSO, using insights from swarm behaviour, leading to improvement in cluster quality can be used. This paper presents a new image segmentation algorithm called Adaptive Particle Swarm Optimization and Fuzzy C-means Clustering Algorithm (APSOF), which is based on Adaptive Particle Swarm Optimization (APSO) and Fuzzy C-means clustering. Experimental results show that APSOF algorithm has edge over FCM in correctly identifying the optimum cluster centers, there by leading to accurate classification of the image pixels. Hence, APSOF algorithm has superior performance in comparison with classic Particle Swarm Optimization (PSO) and Fuzzy C-means clustering algorithm (FCM) for image segmentation.
