The Complexity of Multiterminal Cuts
SIAM Journal on Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Salient Closed Boundary Extraction with Ratio Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlation clustering in general weighted graphs
Theoretical Computer Science - Approximation and online algorithms
Combined Top-Down/Bottom-Up Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry of Cuts and Metrics
What, where and how many? combining object detectors and CRFs
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
The max-cut problem on graphs not contractible to K5
Operations Research Letters
Probabilistic image segmentation with closedness constraints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Layered Object Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Break and conquer: efficient correlation clustering for image segmentation
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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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 optimization techniques in minimizing the objective and is competitive with the state of the art in producing high-quality segmentations.