基于进化规划的Markov随机场参数的估计

Parameter Estimation in Markov Random Field Based on Evolutionary Programming

在应用 Markov 随机场作为先验模型对图像进行贝叶斯估计时.配分函数的难以计算使得对 Markov 随机场参数的估计存在着很大困难.为此,本文提出一种新的基于进化规划的参数估计法。该方法采用进化规划来寻求合适的参数.使得由该参数得到的生成图像和原始图像间的差异最小.该方法不仅可避免配分函数计算上的困准.而且从该参数出发还可得到最相似于(可完全吻合)原始图像的生成图像.在这一点上.该方法要明显优于以往传统的甚于似然函数的参数估计法.如极大伪似然法.最终的实验结果也证实了该方法的可行性.

It＇s difficult to estimate the parameters in Markov Random Field （MRF） due to the computationally intractable partition function when using Markov random field as the prior model of image in Bayesian method . So a new method based on Evolutionary Programming （ EP ） is presented to estimate these parameters in this paper. This method employs evolutionary programming to search for the suited parameters , from which the most similar simulated image of the original image can be obtained . Using this method , the calculation of the computationally intractable partition function can be avoided. Moreover, the most similar （even the entirely identical） simulated image of the original image can be obtained, which makes this method better than other traditional methods based on the likelihood function . Finally , this method is verified by experimental results.