A Fast Fuzzy C-Means Clustering for Color Image Segmentation
快速模糊C均值聚类彩色图像分割方法

A new fast fuzzy C-means(FCM) clustering without a priori information about the number of clusters for color image segmentation is proposed to solve the problem of heavy calculating burden and the disadvantage that clustering performance is affected by initial centers for FCM, which is simple and easy to implement in color image segmentation. It uses the hierarchical subtractive clustering(HSC), which could reduce the heavy computation load when clustering a large number of data points, to partition the image data into a certain number of subsets with similar color. For one thing, the centers of the subsets are used to initialize cluster centers; for another, centers of the subsets and the number of points in the neighborhood of centers are used in FCM. The computation speed of the fuzzy clustering algorithm is improved greatly because the number of color image data points used in fuzzy clustering is reduced notably and the computing load of HSC is much less than that of subtractive clustering. Furthermore, it can use the cluster validity index to find the number of clusters quickly. Experiments show that without changing the clustering function, the proposed approach has much faster computation speed than plain FCM algorithm and can segment the color image quickly and effectively.