假设图像中各像素灰度值是具有一定概率分布的随机变量，由贝叶斯定理，正确分割观测图像等价于求出具有最大后验概率的实际图像估计。在此框架下，提出了一种改进型Graph Cuts图像分割算法。与传统Graph Cuts分割算法相比，该算法在模型建立上有两个方面的改进：1)将模糊C均值聚类引入数据约束能量函数来得到各像素在某个标记下的概率，改善了收敛性能;2)使用非参数方法估计图像的统计分布，然后用此统计量构成图像分割的先验概率，并保证分割结果的局部平滑。由于非参数估计是由样本直接估计得到的结果，特别适用于小样本和分布函数不恒定的情况，因此拓展了算法的适用范围。实验结果表明，改进算法在遥感图像分割和医学图像分割中均提高了分割精度，证明了该算法的有效性。

Suppose each pixel of an image is a random variable under some kind of probability distribution, according to the Bayes theorem, the segmentation of the original images is equivalent to their maximum a posteriori probability estimation. In this framework, we proposed an improved image segmentation algorithm based on Graph Cuts. The construction of the original Graph Cuts model is improved in two aspects. First, fuzzy C-means clustering is introduced into the energy function of data restriction. With the help of fuzzy clustering method, the energy function’s performance of constringency is improved. Second, nonparametric method is used to estimate the statistical distribution of the image, which work as the prior probability used in image segmentation. With the presented method, the results of segmentation are guaranteed to be smooth locally. Since the nonparametric estimation is directly evaluated from the samples, and is suitable for situations of small samples and variable distribution functions, the applicability of our algorithm is extended. Experimental results have shown that the proposed algorithm has good performance on segmenting remote sensing images and medical images.