This paper defines the Feature Symbol Random Field（FSRF）

while presents a novel FSRF-Gibbs model for texture segmentation.The main function of FSRF is acting as 2D representation of texture feature vectors which come from the multichannel analysis. What should be emphasized is that all the employed features are spatial-changed

i. e. need not to be stable for certain texture region. By employment of FSRF

this poper also isgnificantly eases the problem in model estimation of Markov Random Field（MRF）. As a result

finer and more reasonable segmentation is expected by involving both multichannel analysis techniques and fine MRF model. Finally

a new algorithm is included

which is easy to perform and leads to satisfactory experiment results on Bredatz Textures.