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Lecture Notes in Computer Science
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 Pla-narCC solverdoi:10.1007/978-3-319-14612-6_36 fatcat:y6sv2qwtanabhpffitbtyr7wwy