基于马尔可夫随机场模型和函数联接网络的纹理分类方法

Texture classification based on Markov random field model and function link network

文中提出了利用二阶高斯MRF模型对图像纹理进行描述 ,采用最小平方误差方法进行参数估计 ,将估计参数作为纹理的特征向量 ,并且利用函数联接网络对特征进行分类。对纹理图像进行的实验表明 ,采用这种方法能够提高学习速度 ,简化计算过程 ,纹理分类能取得较好的效果。

A texture classification approach is proposed. Image texture is expressed by the second order Gauss MRF model, and the least square error estimation is employed for the solution of model parameters. To improve the performance of texture classification, the function link network is introduced. Experiment shows that better classification results can be obtained than traditional Euclidean distance method, and it has the advantages of simple processing procedure and fast convergence speed.