基于概率图模型的图像纹理模型

 纹理作为一种视觉特征，它广泛应用于图像分析。概率图模型由于其自身特点可以很好地描述纹理。高斯图模型结构可根据局部马尔科夫性和高斯变量的条件回归之间的关系来学习。高斯图模型可用一个邻域系统、一个参数集和一个噪声序列表示。利用惩罚正则化方法，可以选择高斯图模型的邻域并估计参数，然后提取纹理特征进行纹理合成和分类。实验结果显示基于高斯图模型的纹理特征更加有效。

Texture Models Based on Probabilistic Graphical Models

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.