纹理作为一种重要的视觉特征，广泛应用于图像分析。高斯图模型（GGM）可以很好地描述有交互作用的高维数据，因此可用来建立图像纹理模型。根据纹理特征的局部马尔可夫性和高斯变量的条件回归之间的关系，将复杂的模型选择转变为较简单的变量选择，应用惩罚正则化技巧同步选择邻域和估计参数。提取基于图模型的纹理特征分析纹理，实验显示了很好的效果。因此，利用高斯图模型来构建纹理模型有很好的发展前景。

Texture is one of the important visual features in image analysis. Gaussian graphical models (GGM) have good prospective, and are applied to construct the texture model. The structure of the GGM is explored by the connection between the local Markov property of texture features and the conditional regression of Gaussian random variables. Thus, the model selection can be converted to select variables. The technique of penalty regularization provides many methods for variable selection and parameter estimation. Furthermore, the methods of penalty regularization conduct neighborhood selection and parameter estimation simultaneously. The texture feature is extracted and applied in texture analysis.