Energy minimization-based mixture model for image segmentation

A novel mixture model with spatial constraint is proposed for image segmentation. This model assumes that the pixel label prior probabilities are similar if the pixels are geometric close. An energy function is defined on the spatial space for measuring the spatial information. We also derive an energy function on the observed data space from the log-likelihood function of the standard mixture model. We estimate the model parameters and posterior probability by minimizing the combination of the two energy functions, using the gradient descent algorithm. Numerical experiments are presented where the proposed method is tested on synthetic and real world images. These experimental results demonstrate that the proposed method achieves competitive performance compared to spatially variant finite mixture model.