Image Segmentation via Probabilistic Graph Matching
This addresses image segmentation for computer vision applications, but it appears incremental as it builds on existing probabilistic and graph-based approaches.
The paper tackles unsupervised and semi-automatic image segmentation by formulating it as an inference problem using probabilistic graph matching, and it shows favorable comparison with contemporary methods on state-of-the-art image sets.
This work presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as a inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is solved via a probabilistic graph matching scheme, which allows rigorous incorporation of low level image cues and automatic tuning of parameters. The proposed scheme is experimentally shown to compare favorably with contemporary semi-supervised and unsupervised image segmentation schemes, when applied to contemporary state-of-the-art image sets.