Fast Planar Correlation Clustering for Image Segmentation
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Abstract
We describe a new optimization scheme for finding high- quality 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 optimiza- tion techniques in minimizing the ob jective and is competitive with the state of the art in producing high-quality segmentations.
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