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.