中文说明：
本文以k-means算法为背景，引入信息熵相关知识，从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时，会发生一定的过拟合现象，导致我们得不到预期的聚类数目。本文设计合理的合并准则，令模型简化，有效地消除过拟合现象，使得最后得到的聚类数目与预期符合。设计合理的准则改进了图像的全自动分割方法，使得分割结果更加优化
English Description：
In this paper, the k-means algorithm as the background, the introduction of information entropy related knowledge, so as to achieve automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over fitting phenomenon, which leads to that we can not get the expected number of clusters. In this paper, a reasonable combination criterion is designed to simplify the model and effectively eliminate the over fitting phenomenon, so that the number of clusters obtained is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method, which makes the segmentation result more optimized