Fast Planar Correlation Clustering for Image Segmentation

@inproceedings{Yarkony2012FastPC,
  title={Fast Planar Correlation Clustering for Image Segmentation},
  author={Julian Yarkony and Alexander T. Ihler and Charless C. Fowlkes},
  booktitle={European Conference on Computer Vision},
  year={2012}
}
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… 
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67 Citations
Highly Influential Citations
2
Background Citations
35
Methods Citations
23

67 Citations

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Break and Conquer: Efficient Correlation Clustering for Image Segmentation

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Feature-aligned segmentation using correlation clustering

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Fusion moves for correlation clustering

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Higher-order segmentation via multicuts

Segmenting Planar Superpixel Adjacency Graphs w.r.t. Non-planar Superpixel Affinity Graphs

This work considers minimum multicuts of superpixel affinity graphs in which all affinities between non-adjacent superpixels are negative and proposes a relaxation by Lagrangian decomposition and a constrained set of re-parameterizations for which it can optimize exactly and efficiently.

Analyzing PlanarCC : Demonstrating the Equivalence of PlanarCC and The Multi-Cut LP Relaxation

The linear programming (LP) relaxation corresponding to PlanarCC and the multi-cut LP relaxation which are two methods for correlation clustering are studied and it is demonstrated that they have equal value when optimized.
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