Hierarchical Planar Correlation Clustering for Cell Segmentation
We introduce a novel algorithm for hierarchical clustering on planar graphs we call “Hierarchical Greedy Planar Correlation Cluster- ing” (HGPCC). We formulate hierarchical image segmentation as an ul- trametric rounding problem on a superpixel graph where there are edges between superpixels that are adjacent in the image. We apply coordi- nate descent optimization where updates are based on planar correlation clustering. Planar correlation clustering is NP hard but the eﬃcient Pla- narCC solver allows for eﬃcient and accurate approximate inference. We demonstrate HGPCC on problems in segmenting images of cells.
Text ReferenceJulian Yarkony, Chong Zhang, and Charless C. Fowlkes. Hierarchical planar correlation clustering for cell segmentation. In Energy Minimization Methods in Computer Vision and Pattern Recognition, pages 492–504. 2015.
author = "Yarkony, Julian and Zhang, Chong and Fowlkes, Charless C.",
title = "Hierarchical Planar Correlation Clustering for Cell Segmentation",
booktitle = "Energy Minimization Methods in Computer Vision and Pattern Recognition",
year = "2015",
pages = "492--504",
tag = "biological_images,grouping"