We present a pixel-related Gaussian mixture model (GMM) to segment images into superpixels.  GMM is a weighted sum of Gaussian functions, each one corresponding to a superpixel, to describe the density of each pixel represented by a random variable.  Different from previously proposed GMMs, our weights are constant, and Gaussian functions in the sums are subsets of all the Gaussian functions, resulting in segments of similar size and an algorithm of linear complexity with respect to the number of pixels. 
GMM
Superpixel Segmentation Using Gaussian Mixture Model.Superpixel segmentation partitions an image into perceptually coherent segments of similar size, namely, superpixels.  We present a pixel-related Gaussian mixture model (GMM) to segment images into superpixels. 
Gaussian Mixture Model