关键词: 图像分割, 分裂式K均值, 模糊C均值, 聚类, 无监督

Abstract: Fuzzy CMeans (FCM) clustering algorithm is an efficient unsupervised segmentation method, which is suitable for any classification number without the need to predict the image characteristics, but its clustering result has direct impact from sample noise component and the set of initial conditions. Therefore, a Fissive K-Means(FKM) clustering algorithm for color image segmentation was proposed, which firstly denoised the sample data using median filtering, then preclassified the image samples according to a fissive clustering method to get an initial partition of sample set, finally iteratively optimized segmentation using K-means clustering based on the rule of probability distance from the initial partition. The experimental results show that the algorithm can avoid the misclassification of FCM such as dead center, center overlapping and local minima, and accelerate the segmentation speed.

Key words: image segmentation, Fissive K-Means (FKM), Fuzzy C-Means (FCM), clustering, unsupervised