模糊C均值(FCM)聚类用于彩色图像分割具有简单直观、易于实现的特点，但存在聚类性能受中心点初始化影响且计算量大等问题，为此，提出了一种快速模糊聚类方法(FFCM)。这种方法利用分层减法聚类把图像数据分成一定数量的色彩相近的子集，一方面，子集中心用于初始化聚类中心点；另一方面，利用子集中心点和分布密度进行模糊聚类，由于聚类样本数量显著减少以及分层减法聚类计算量小，故可以大幅提高模糊C均值算法的计算速度，进而可以利用聚类有效性分析指标快速确定聚类数目。实验表明，这种方法不需事先确定聚类数目并且在优化聚类性能不变的前提下，可以使模糊聚类的速度得到明显提高，实现彩色图像的快速分割。

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.