1. A process for segmenting an image of an image sequence, comprising the process actions of: using a computing device for: (a) designating foreground regions in an initial image of an image sequence; (b) defining certain regions of the initial image as foreground and certain regions of the initial image as background based on the designated foreground regions; (c) learning a foreground spatial-color Gaussian Mixture Model (SCGMM) model which describes the foreground regions and background SCGMM model that describes the background regions of the initial image, wherein the SCGMM is defined in spatial coordinates and color space; (d) segmenting the initial image using a graph cut procedure which minimizes a Markov random field energy function containing the learned foreground and background SCGMM models; (e) inputting a next image of the image sequence; (f) updating the foreground and background SCGMM models using a joint SCGMM tracking procedure, wherein only the spatial parameters of the foreground and background SCGMM models are updated while the color parameters remain unchanged from a previous image in the image sequence, wherein updating the foreground and background SCGMM models using the joint tracking procedure comprises: combining the foreground and background SCGMM models of a previous image into a generative model, running a constrained Expectation Maximization procedure to maximize the joint likelihood of the next input image comprising fixing color components of the generative model and updating the spatial components and weights of the spatial components of the generative model, and separating the generative model back into an updated foreground SCGMM model and an updated background SCGMM model; (g) performing a graph cut procedure which minimizes a Markov Random Field energy function containing the updated foreground and background SCGMM models to segment the next image of the sequence; and (h) updating each of the foreground and background SCGMM models with the segmented result of process action (g).