  We describe a new optimization scheme for finding high-quality correlation
clusterings in planar graphs that uses weighted perfect matching as a
subroutine. Our method provides lower-bounds on the energy of the optimal
correlation clustering that are typically fast to compute and tight in
practice. We demonstrate our algorithm on the problem of image segmentation
where this approach outperforms existing global optimization techniques in
minimizing the objective and is competitive with the state of the art in
producing high-quality segmentations.
