关键词: 隐狄利克雷分布, 期望最大化方法, 贝叶斯模型, 高斯混合模型, 图像分割

Abstract: 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).

Key words: Latent Dirichlet Allocation (LDA), Expectation Maximization (EM) method, Bayesian model, Gaussian Mixture Model (GMM), image segmentation