基于邻域场拉普拉斯混合模型图像分割的研究

Laplacian mixture model with neighborhood field for image segmentation

针对高斯混合模型(GMM)不能有效处理重尾噪声下图像拖尾情况,提出了基于拉普拉斯(Laplacian)分布的有限混合模型图像分割方法。与标准拉普拉斯混合模型(LMM)将像素点作为孤立个体不同的是,该方法充分考虑了相邻像素点间的空间关系。相较传统混合模型参数估计采用的EM算法,该方法采用梯度下降法优化参数。实验结果表明在处理重尾噪声时,该方法与标准LMM算法和GMM算法相比,鲁棒性更好,分割更精确有效。

Finite mixture model under Laplacian distribution is proposed in order to solve the problem that image segmentation based on Gaussian Mixture Model（GMM） failing to settle tailing situation with heavy-tailed noise, besides, unlike the standard Laplacian Mixture model（LMM） where pixels themselves are considered independent of each other, the proposed method incor- porates the spatial neighborhood relationship ofpixels into the standard LMM. In order to estimate model parameters from obser- vations and instead of utilizing an expectation-maximization algorithm, the gradient method is adopted. The experimental results demonstrate the robustness, accuracy, and effectiveness of the method in comparison with the standard LMM and GMM.