2015
NIPS
NeurIPS 2015
Planar Ultrametrics for Image Segmentation
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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Interdisciplinary Bridge
— Computer Vision and Data Science & Analytics and Machine Learning
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Keyword Pioneer
— minimum cost perfect matching
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Hot Topic Early Bird
— graph matching
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio