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.
CITATION STYLE
Yarkony, J., Zhang, C., & Fowlkes, C. C. (2015). Hierarchical planar correlation clustering for cell segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8932, pp. 492–504). Springer Verlag. https://doi.org/10.1007/978-3-319-14612-6_36
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