Fast Planar Correlation Clustering for Image Segmentation
This addresses the problem of efficient and accurate image segmentation for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of finding high-quality correlation clusterings in planar graphs for image segmentation by introducing a new optimization scheme using weighted perfect matching as a subroutine, which outperforms existing global optimization techniques in minimizing the objective and is competitive with state-of-the-art methods in producing high-quality segmentations.
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.