Color image segmentation using a self-initializing EM algorithm

This paper presents a new method based on the Expectation-Maximization (EM) algorithm that we apply for color image segmentation. Since this algorithm partitions the data based on an initial set of mixtures, the color segmentation provided by the EM algorithm is highly dependent on the starting condition (initialization stage). Usually the initialization procedure selects the color seeds randomly and often this procedure forces the EM algorithm to converge to numerous local minima and produce inappropriate results. In this paper we propose a simple and yet effective solution to initialize the EM algorithm with relevant color seeds. The resulting self initialised EM algorithm has been included in the development of an adaptive image segmentation scheme that has been applied to a large number of color images. The experimental data indicates that the refined initialization procedure leads to improved color segmentation

Skin detection using the EM algorithm with spatial constraints

In this paper, we propose a color-based method for skin detection and segmentation, which also takes into account the spatial coherence of the skin pixels. We treat the problem of skin detection as an inference problem. We assume that each pixel in an image has a hidden binary label associated with it, that specifies if it is skin or not. In order to solve the inference problem, we use a variational EM algorithm, which incorporates the spatial constraints with just a small computational overhead in the E-step. Finally, we show that our method provides better results than the standard EM algorithm and a state-of-art skin-detection method from the literature of Jones, M. J. and Rehg, J. M. (2002)