The application of classical Gaussian mixture model to image segmentation has highly computational complexity. A image segmentation method based on Gaussian mixture model with multi-sampling is proposed in order to solve this problem. First

the sampling theorem is given and proved

and the minimum sample size is derived according to the smallest cluster and cluster number. Second

a penalty function

which is to judge the good sample

has been designed to eliminate the error of clustering model

and image pixels are sampled based on the minimum sample size to be clustered according to Gaussian mixture model. Finally

by the means of the definition on the distance between a pixel point and the categories

the remaining points is assigned respective cluster depending on the principles of the nearest distance. The experimental results show the effectiveness of the algorithm.