Image Segmentation via Probabilistic Graph Matching
Ayelet Heimowitz Yosi Keller
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.