Planar Ultrametrics for Image Segmentation

NeurIPS 2015 pp. 64-72

/neurips/2015/yarkony2015neurips-planar/

Abstract

We study the problem of hierarchical clustering on planar graphs. We formulate this in terms of finding the closest ultrametric to a specified set of distances and solve it using an LP relaxation that leverages minimum cost perfect matching as a subroutine to efficiently explore the space of planar partitions. We apply our algorithm to the problem of hierarchical image segmentation.

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Cite

Text

Yarkony and Fowlkes. "Planar Ultrametrics for Image Segmentation." Neural Information Processing Systems, 2015.

Markdown

[Yarkony and Fowlkes. "Planar Ultrametrics for Image Segmentation." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/yarkony2015neurips-planar/)

BibTeX

@inproceedings{yarkony2015neurips-planar,
  title     = {{Planar Ultrametrics for Image Segmentation}},
  author    = {Yarkony, Julian E and Fowlkes, Charless},
  booktitle = {Neural Information Processing Systems},
  year      = {2015},
  pages     = {64-72},
  url       = {https://mlanthology.org/neurips/2015/yarkony2015neurips-planar/}
}