Spatial information is often used to enhance the robustness of traditional fuzzy c-means (FCM) clustering algorithms. Although some recently emerged improvements are remarkable, the computational complexity of these algorithms is high, which may lead to lack of practicability. To address this problem, an efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented. First, a fuzzy clustering algorithm with multi-pixel fitting spatial information (FCM-MF) is developed. Specifically, by dividing the input image into several filter windows, the spatial information of all pixels in each filter window can be obtained simultaneously by fitting the pixels in its corresponding neighbourhood window, which enormously reduces the computational complexity. However, the FCM-MF may result in the loss of edge information. Therefore, the FCM-VMF integrates a variable window strategy with FCM-MF. In this strategy, to preserve more edge information, the sizes of the filter window and generalized neighbourhood window are adaptively reduced. The experimental results show that FCM-VMF is as effective as some recent algorithms. Notably, the FCM-VMF has extremely high efficiency, which means it has a better prospect of application.

空间信息通常用于增强传统模糊 c 均值 (FCM) 聚类算法的鲁棒性。虽然最近出现的一些改进效果显着，但这些算法的计算复杂度很高，可能导致缺乏实用性。为了解决这个问题，提出了一种称为具有可变多像素拟合空间信息的模糊聚类算法（FCM-VMF）的有效变体。首先，开发了一种具有多像素拟合空间信息的模糊聚类算法（FCM-MF）。具体来说，通过将输入图像划分为若干个过滤窗口，通过拟合其对应邻域窗口中的像素，可以同时获得每个过滤窗口中所有像素的空间信息，极大地降低了计算复杂度。然而，FCM-MF 可能会导致边缘信息的丢失。因此，FCM-VMF 将可变窗口策略与 FCM-MF 集成在一起。在该策略中，为了保留更多的边缘信息，自适应减小滤波器窗口和广义邻域窗口的大小。实验结果表明，FCM-VMF 与一些最近的算法一样有效。值得注意的是，FCM-VMF具有极高的效率，这意味着它具有更好的应用前景。