Fusion of Image Segmentations under Markov, Random Fields

Abstract

In this study, a fast and efficient consensus segmentation method is proposed which fuses a set of baseline segmentation maps under an unsupervised Markov Random Fields (MRF) framework. The degree of consensus among the segmentation maps are estimated as the relative frequency of co-occurrences among the adjacent segments. Then, these relative frequencies are used to construct the energy function of an unsupervised MRF model. It is well-known that MRF framework is commonly used for formulating the spatial relationships among the super-pixels, under the Potts model. In this study, the Potts model is reorganized to represent the degree of consensus among the spatially adjacent segments (super-pixels). The proposed segmentation fusion method, called, Boosted-MRF, is tested in various experimental setups, and its performance is compared to the state of the art segmentation methods and satisfactory results are obtained.