图模型在彩色纹理分类中的应用

纹理分析中往往将彩色图像转换为灰度图以降低计算复杂度，这样就忽略了颜色信息。而利用主成分分析 的方法来降维彩色纹理，则可以尽可能地保留颜色和纹理信息。高斯图模型(Uaussian Graphical Models, 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。根据局部马尔可夫性和高斯变量的条件回归之 间的关系，可将复杂的模型选择转变为较简单的变量选择。通过惩罚正则化方法，其部域选择和参数佑计可同步进 行，然后提取纹理特征进行彩色纹理分类，实验显示其具有很好的效果。因此，结合主成分分析和高斯图模型来构建 彩色纹理模型有很好的发展前景。

Applications of Graphical Models in Color Texture Classification

Texture is one of the important visual features in image analysis. For convenience, color texture images are often converted to gray images. It is a pity that color information is ignored. In order to keep texture and color informa- tion,principle component analysis (PCA) was utilized to reduce the dimension of color textures. Gaussian graphical models (GGM) have good prospect due to themselves advantages, and are applied to construct texture model. The structure of GGM is explored by the connection between the local Markov property and conditional regression of Gauss- ian random variables. Thus, the model selection can be converted to select variables in GGM. The development of tech- nic}ue of penalty regularization provides many methods for variable selection and parameter estimation. And, the methods of penalty regularization conduct neighborhood selection and parameter estimation simultaneously. Then, the texture feature is extracted and applied in color texture classification. The experiments show the good results. Therefore, the texture models based connection of GGM and PCA have an attractive prospect.