Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning

NeurIPS 2019 Enrique Fita SanmartinSebastian DamrichFred A. Hamprecht

The seeded Watershed algorithm / minimax semi-supervised learning on a graph computes a minimum spanning forest which connects every pixel / unlabeled node to a seed / labeled node. We propose instead to consider all possible spanning forests and calculate, for every node, the probability of sampling a forest connecting a certain seed with that node... (read more)

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