图像分割中改进空间约束贝叶斯网络模型的应用

针对马尔可夫链蒙特卡罗方法普遍存在的迭代收敛性问题，在具有空间平滑约束的高斯混合模型条件上提出改进空间约束贝叶斯网络模型并在图像分割领域进行具体应用。所提模型应用隐狄利克雷分布（LDA）概率密度模型和高斯-马尔可夫定理的随机域参数混合过程来实现参数平滑。所提方法根据空间信息先验平滑变换操作，在待处理像素点的上下文混合结构中引入LDA符合多项式分布，用来替换传统期望最大化算法中映射操作。LDA参数采用闭合形式将有利于准确估计最大后验概率（MAP）框架与上下文混合结构的相关比例。实验结果表明，应用PRI、VoI、GCE和BDE指标进行效果比较，该方法比联合系统工程组（JSEG）、当前变换矩阵（CTM）和最大后验概率-最大似然法（MM）方法的图像分割应用效果较好，高斯噪声对于该算法的鲁棒性影响较小。

Application of improved spatially constrained Bayesian network model to image segmentation

Aiming at the problem of iterative convergence of Markov chain Monte Carlo method, an improved spatially constrained Bayesian network model was proposed and applied in the image segmentation domain based on the Gaussian mixture model with spatial smoothing constraint. Latent Dirichlet Allocation (LDA) probability density model and the parameter mix process of Gauss-Markov theorem were used to achieve parameter smoothing. According to the spatial information transcendental transformation operation, the LDA conformance polynomial distribution was introduced into the context hybrid structure of the pixel to be used to replace the mapping operation in the traditional expectation maximization algorithm. LDA parameters were represented by a closed form, which facilitated to accurately estimate the relative proportion of MAP (Maximum A Posteriori) framework to context mixture structure. The experimental results in terms of PRI (Probabilistic Rand Index), VoI (Variation of Information), GCE (Global Consistency Error) and BDE (Boundary Displacement Error) show that the proposed method has better effect in image segmentation, its robustness is less influenced by Gauss noise compared with JSEG (Joint Systems Engineering Group), CTM (Current Transformation Matrix) and MM (Maximum A Posteriori Probability-Maximum Likelihood).