A novel segmentation method based on Markov Random Field（MRF） and Support Vector Machine（SVM） posteriori probability is proposed in the paper.As a rule

image segmentation using MRF model has two steps.Firstly

distribution of conditional probability of pixel characteristic is obtained by the parameter estimate for probability density and then maximum a posteriori（MAP） principle is always used to gain the optimum estimate of class label.In practice

the hypothesis of Gauss distribution model is always adopted

but it is not the model fit for any images

for example

SAR images often fit to a model of Rayleigh distribution and especially some texture images

it is very difficult to deduce an accurate distribution model.In order to solve the two major problems which are the complexity of parameter estimate in using the distribution of conditional probability and the difficulty of deducing an accurate distribution in theoretical way

the new segmentation approach based on MRF and SVM posteriori probability is proposed.Support Vector Machine is a set of related supervised learning method

it is a classification technique based on the structural risk minimization principle and it maps input vectors to a higher dimensional space where maximal separating two parallel hyperplanes are constructed.An assumption is made that the larger the margin or distance between these parallel hyperplanes the better the generalisation error of the classifier will be.However

in the pattern recognition practice

people need soft decision

that is to say

not only gain class label which the sample belongs to

but also obtain the membership degree of sample in each class label

that is posteriori probability of sample.The new segmentation algorithm proposed by the paper follows three steps.Firstly

the paper adopts the Platt’s method to obtain the posteriori probability by mapping the output of SVM decision-function after training.Secondly

it converts the conditional probability estimate into posteriori probability estimate in terms of Bayes formula

and then proposes a new segmentation method which depends on MRF model based on posteriori probability.Finally

it brings the information of posteriori estimate into MRF model

thus the posteriori probability based on SVM is combined with MRF in the application of image segmentation.Two experiments has been conducted

one is that the paper selects twelve texture images from Brodatz standard texture database to make up some merged texture images.The number of training samples in each texture class is 30 and eight features are used to characterize each texture sample.The results of three group segmentation experiments all show that the new method appears preferable to Gaussian MRF method.The other experiment is that the synthesis texture image is composed by SAR texture images

SAR images do not fit for Gauss distribution

as a result

Gaussian MRF method results in high misclassification rate and low robustness.On the contrary

the proposed algorithm depends on the information of posteriori probability estimate based on SVM without the hypothesis of sample conditional probability

so it achieves a higher level of robustness and segmentation results demonstrating its efficiency.