Overview of Gaussian mixture models,solving algorithms and visual applications
高斯混合模型、求解算法及视觉应用综述

Gaussian Mixture Models(GMMs) is the basic model of statistical machine learning and widely applied to visual media fields. In recently years, with the rapid growth of visual media information and deep development of analytical techniques GMMs have obtained further developments in such fields as (texture) image segmentation, video analysis, image registration and clustering. This paper begins from the basic models of GMMs, discusses and analyzes from both theoretical and application aspects the solving methods of GMMs including EM algorithms and its variants, and expounds the two problems of model selection: online learning and model reduction. In visual applications, this paper introduces GMM-based models and methods in image segmentation, video analysis, image registration and image de-noising, expatiates the principles and processes of some newest and classical models, such as space-variant GMMs for image segmentation, coherent point draft algorithm for image registration. At last, this paper gives some possible latent directions and difficult problems.