Non-homogeneous Conditional Random Fields for Contextual Image Segmentation

We propose a non-homogeneous conditional random field (CRF) built over an adjacency graph of superpixels for contextual region grouping. Our model includes spatially dependent potentials that capture contextual interactions of the data as well as the labels. Both superpixels and segments are described with local statistics which take into account their contexts in the image. This results the non-homogeneity of the fields which improves the region grouping process of natural images. In our energy formulation, the similarity is measured by a likelihood ratio learned from a human labeled ground truth. The inference is performed using a cluster sampling method, the Swendsen-Wang cut algorithm. Results are shown on various natural images.