马尔可夫随机场约束下的PCM图像分割算法

与模糊c均值（FCM）算法相比较，可能性C均值（PCM）聚类算法具有更好的抗干扰能力。但PCM聚类算法对初始化条件很敏感，在聚类的过程中很容易导致聚类结果一致性，并且没有考虑到像素的空间信息，用在图像分割尤其是多目标图像分割上效果极不稳定。在PCM算法的基础上，利用Markov随机场中的邻域关系属性，引入先验空间约束信息，建立包含灰度信息与空间信息的新聚类目标函数，提出马尔可夫随机场与PCM聚类算法相融合的图像分割新算法（MP．CM算法）。实验结果表明，在多目标图像分割上利用MPCM算法可以取得比PCM更好的分割效果。

Image segmentation on Possibilistic C-Means clustering algorithmbased on Markov spatial constraint

Compared with Fuzzy C-Means （FCM） clustering, Possibilistic C-Means （PCM） has a better anti jamming capability. But the Possibilistic C-Means clustering is very sensitive to initial conditions and is very easy to cause the clustering result of consistency. And it doesn＇ t take into account the pixel spatial information. It is extremely unstable when it is used in image segmen- tation especially in multi-object image segmentation. Based on the PCM clustering, the prior spatial constraint is incorporated according to Markov random field theory, to build a new clustering objective function including the establishment of gray information and spatial information. This paper presents a new image segmentation algorithm（MPCM） combining Markov and PCM clustering. With experiments, using MPCM algorithm can achieve a better segmentation result than PCM in multi-object image segmentation.