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

The authors prove that the probabilities they obatin are equivalent to the probabilities yielded by the Random Walker algorithm. The authors state that this result has been shown in the original Random Walker work, yet is little known, and their proof is different and more self-contained, not relying on potential theory. Excitingly, their way of proof yields a novel interpretation of the Random Walker / Probabilistic Watershed probabilities in terms of the triangle equation on effective resistances between graph nodes. Last but not least the authors relate their theory to the Power Watershed, again yielding an exciting new insight, namely that for parameters beta 2 and alpha towards infinity, the latter computes marginals over all seed-separating *maximum* spanning forests (i.e.