快速自动模糊C-均值聚类彩色图像分割算法

Fast and Automatic Fuzzy C-Means Clustering Color Image Segmentation Algorithm

针对传统模糊C-均值(FCM)聚类算法计算复杂度高、无法自动确定聚类数目的问题,提出了一种快速自动FCM聚类彩色图像分割算法。首先通过改进的简单线性迭代聚类(SLIC)超像素算法预分割图像,将传统基于单个像素的聚类转化为基于超像素区域的聚类,降低FCM计算复杂度;其次利用改进的密度峰值算法自动确定聚类数目,提高算法灵活性;最后,对超像素图像进行基于直方图的FCM聚类,完成图像分割。为验证所提算法的有效性,采用BSDS500、AID和MSRC公共数据库作为实验数据集,并与其他4种FCM分割算法进行了比较。实验结果表明,所提分割算法在分割精准度、模糊分割系数、模糊分割熵和视觉效果等方面均优于其他几种比较算法。

A fast and automatic fuzzy C-means clustering(FCM)color image segmentation algorithm is proposed as an alternative to the traditional FCM algorithm,which has high computational complexity and fails to automatically determine the number of clusters.First,the image is presegmented by an improved simple linear iterative clustering(SLIC)algorithm,transforming the traditional pixel-based clustering into superpixel region-based clustering and reducing computational complexity.Second,the improved density peak algorithm determines the number of clusters automatically and improves flexibility.Finally,superpixel images are subjected to histogram-based FCM clustering to complete image segmentation.The BSDS500,AID,and MSRC public databases were utilized as experimental datasets and compared with other FCM segmentation methods to verify their effectiveness.In terms of segmentation accuracy,fuzzy segmentation coefficient,fuzzy segmentation entropy,and visual effect,the experimental results show that the proposed segmentation algorithm outperforms several other comparative algorithms.