图像纹理基元分类的马尔柯夫随机场方法

提出基于马尔柯夫随机场（MRF）的图像纹理基元分类新方法。利用MRF里中心像元特征值与邻近像元特征值之间的约束关系，反映图像纹理基元的特征以及不同的MRF参数。根据由同一类别的图像求得的MRF参数计算出的标准差最小这一性质来进行图像纹理的分类。通过不同实验方案的对比，以及与不同分类方法的比较，证实提出的图像纹理基元分类方法具有一定的优势。

A Method of Image Texture Texton Classification with Markov Random Field

In this article a new method based on MRF to classify image texture texton has been put forward. The constraint relationship between the center pixel feature value and the neighbor pixels feature value in MRF can reflect the features of image texture texton as well as different MRF parameters. Standard deviation based on the MRF parameter of the same category is the smallest. So we can use this property to classify image texture. By comparing the different experimental scheme and different classification method, we can come to the conclusion that the method of image texture element classification proposed in this paper has certain advantages, and it is a good methold of image classification.