Before revision:  We provide an algorithm for learning and inference , validate it on synthetic data , and illustrate how the two components of our model improve segmentation of natural images . We then show that the posterior distribution over segmentations captures well the variability across human subjects , indicating that our model provides a viable approach to probe human visual segmentation.
After revision:  We provide an algorithm for learning and inference , validate it on synthetic data . Importantly, with the appropriate choice of the prior, we derive an intuitive closed--form update rule for assigning pixels to segments: at each iteration, the pixel assignment probabilities to segments is the sum of the evidence (i.e. local pixel statistics) and prior (i.e. the assignments of neighboring pixels) weighted by their relative uncertainty. The model performs competitively on natural images from the Berkeley Segmentation Dataset (BSD), and we illustrate how the likelihood and prior components improve segmentation relative to traditional mixture models. Furthermore, our model explains some variability across human subjects , indicating that our model provides a viable approach to probe human visual segmentation.