Poster
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
Enrique Fita Sanmartin · Sebastian Damrich · Fred Hamprecht

Wed Dec 11th 10:45 AM -- 12:45 PM @ East Exhibition Hall B + C #81
in Applications -- Image Segmentation »

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. We dub this approach "Probabilistic Watershed". Leo Grady (2006) already noted its equivalence to the Random Walker / Harmonic energy minimization. We here give a simpler proof of this equivalence and establish the computational feasibility of the Probabilistic Watershed with Kirchhoff's matrix tree theorem. Furthermore, we show a new connection between the Random Walker probabilities and the triangle inequality of the effective resistance. Finally, we derive a new and intuitive interpretation of the Power Watershed.

Keywords: Algorithms -> Image Segmentation · Algorithms -> Semi-Supervised Learning; Applications -> Computer Vision; Applications -> Image Segmentation; Theory

Author Information

Enrique Fita Sanmartin (Heidelberg University)
Sebastian Damrich (Heidelberg University)
Fred Hamprecht (Heidelberg University)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors