A new localized superpixel Markov random field for image segmentation

In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel labeling using MRF. But the local interaction in MRF is limited to pixel label comparison. We propose a new localized superpixel Markov random field (SMRF) model to incorporate local data interaction in unsupervised parameter learning. The advantages of the new model include computational efficiency by using superpixel structure and its ability to integrate local knowledge in the learning process. Quantitative evaluation and visual effects show that the new model achieves not only better segmentation accuracy but also lower computational cost than the baseline pixel based model.