混合空间新型贝叶斯网络模型的图像分割应用研究

摘要

Mixture proportions of the existing work don't have clear probability vector models,and some models cannot solve the iterative convergence problem of Markov chain Monte Carlo (MCMC).According to the Gaussian mixture models with spatial smoothing based on constraint,we present a new Bayesian model which can be applied in image segmentation.We use the probability density model of the latent Dirichlet allocation (LDA) and the latent Dirichlet parameters of Gauss-Markov random field (MRF) to achieve parameter smoothing process.The proposed model has two advantages:1) the model for the spatial smoothing constraint defines the probability vector model proportion;2) we use the maximum a-posteriori (MAP) and the expectation maximization (EM) to achieve the update of closed parameters.Experimental results show that the proposed model has better image segmentation performance than the GMM method,and it has been successfully applied in the image segmentation of natural images and natural artistic images with noise.%现有研究工作没有确定概率向量模型的混合部分比例,所以无法解决MCMC方法的迭代收敛性问题.在具有空间平滑约束的高斯混合模型GMM基础上提出新型贝叶斯网络模型并应用于图像分割领域.模型应用隐Dirichlet分布LDA的概率密度模型和Gauss-Markov随机域MRF的隐Dirichlet参数混合过程来实现参数平滑过程,具有如下优点:针对空间平滑约束规范概率向量模型比例;使用最大后验概率MAP和期望最大化算法EM完成闭合参数的更新操作过程.实验表明,本模型比其他应用GMM方法的图像分割效果好.该模型已成功应用到自然图像和有噪声干扰的自然艺术图像分割过程中.