Texture is one of the visual features playing an important role in image analysis. Many applications have been discovered using texture models.Probabilistic graphical models Science

are promising tools for constructing texture models.The problem of learning the structure of GGM for texture classification is addressed. GGM are characterized by a neighborhood

a set of parameters

and a noise sequence due to the connection between the local Markov property and conditional regression of a Gaussian random variable. By use of the methods of model selection to choose an appropriate neighborhood and estimate the unknown parameters for modeling GGM

neighborhood selection and parameter estimation are conducted simultaneously. And then new texture features based on GGM for texture synthesis and texture classification are extracted.Experimental results show that adaptive Lasso estimators are more effective.