关键词: 拉普拉斯混合模型（LMM）, 图像分割, 重尾噪声, 空间邻域关系

Abstract: 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 incorporates the spatial neighborhood relationship of pixels into the standard LMM. In order to estimate model parameters from observations 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.

Key words: Laplacian Mixture Model（LMM）, image segmentation, heavy-tailed noise, spatial neighborhood relationship