IEEE Trans Cybern. 2013 Apr;43(2):751-65. doi: 10.1109/TSMCB.2012.2215849. Epub 2013 Mar 7.
Finite mixture models with symmetric distribution have 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 nonsymmetric form. This paper presents a new nonsymmetric 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 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 with that of other mixture models, demonstrating the robustness, accuracy, and effectiveness of our method.
具有对称分布的有限混合模型已被广泛应用于许多计算机视觉和模式识别问题。然而，在许多应用中，数据的分布是非高斯和非对称的。本文提出了一种新的非对称混合模型用于图像分割。我们方法的优点在于它简单、易于实现，并且直观上有吸引力。在本文中，每个标签都用多个 D 维学生 t 分布进行建模，该分布具有重尾，比高斯分布更稳健。采用期望最大化算法来估计模型参数，并最大化观测数据对数似然的下界。在各种数据类型上进行了数值实验。将所提出的模型的性能与其他混合模型进行了比较，证明了我们方法的稳健性、准确性和有效性。
