A Texture Classification Approach Based on Function Link Network
一种基于函数联接网络的纹理分类方法

This paper presents a texture classification approach based on function link network. Image texture is characterized by the second order Gauss MRF model, and the least square error estimation is employed for the estimation of model parameters. However, these parameters are proved to be inefficient in texture classification. To solve this problem, we introduced a function link network to improve the classification performance. Experiment shows that better classification results can be obtained than traditional euclidean distance approach, and it has the advantage of simple processing procedure and fast convergence speed.