Laplacian mixture model with neighborhood field for image segmentation
Le Luo · Computer Engineering and Applications Journal · 2013
Finite mixture model under Laplacian distribution is proposed in order to solve the problem that image segmentation based on Gaussian Mixture Mode(lGMM)failing to settle tailing situation with heavy-tailed noise, besides, unlike the standard Laplacian Mixture mode(lLMM)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.