Fast Estimation of Gaussian Mixture Models for Image Segmentatio...

利用高斯混合模型实现图像分割The Expectation-Maximization algorithmhas been classically used to find the maximum likelihood estimates of parameters in probabilistic models with unobserved data, for instance, mixture models. A key issue in such problems is the choice of the model complexity. The higher the number of components in the mixture, the higher will be the data likelihood, but also the higher will be the computational burden and data overfitting. In this work we propose a clustering method based on the expectation maximization algorithm that adapts on-line the number of components of a finite Gaussian mixture model from multivariate data. Or method estimates the number of components and their means and covariances sequentially, without requiring any careful initialization.