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.