Hierarchical Planar Correlation Clustering for Cell Segmentation

  • Julian Yarkony
  • Chong Zhang
  • Charless C. Fowlkes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8932)

Abstract

We introduce a novel algorithm for hierarchical clustering on planar graphs we call “Hierarchical Greedy Planar Correlation Clustering” (HGPCC). We formulate hierarchical image segmentation as an ultrametric rounding problem on a superpixel graph where there are edges between superpixels that are adjacent in the image. We apply coordinate descent optimization where updates are based on planar correlation clustering. Planar correlation clustering is NP hard but the efficient PlanarCC solver allows for efficient and accurate approximate inference. We demonstrate HGPCC on problems in segmenting images of cells.

Keywords

Image Segmentation Planar Graph Coordinate Descent Cell Segmentation Correlation Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Julian Yarkony
    • 1
  • Chong Zhang
    • 2
  • Charless C. Fowlkes
    • 3
  1. 1.Experian Data LabSan DiegoUSA
  2. 2.CellNetworksUniversity of HiedelbergGermany
  3. 3.Department of Computer ScienceUniversity of CaliforniaIrvine

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