A robust non-symmetric mixture models for image segmentation
Finite mixture model with symmetric distribution has been widely used for many computer vision and pattern recognition problems. However, in many applications, the distribution of the data has a non-Gaussian and non-symmetric form. This study presents a new non-symmetric mixture model for image segmentation. The advantage of our method is that it is simple, easy to implement and intuitively appealing. In this paper, each label is modeled with multiple D-dimensional Student's-t distribution, which is heavily tailed and more robust than Gaussian distribution. Expectation maximization (EM) algorithm is adopted to estimate model parameters and to maximize the lower bound on the data log-likelihood from observations. Numerical experiments on various data types are conducted. The performance of the proposed model is compared to other mixture models, demonstrating the robustness, accuracy and effectiveness of our method.