||Instance segmentation can be cast as a graph partitioning problem. The majority of models developed in this context have relied on purely attractive interactions between graph nodes. To obtain more than a single cluster, it is then necessary to manually set a merging or splitting threshold, or to pre-specify a desired number of clusters.
A notable exception to the above is multicut partitioning / correlation clustering, which allows for repulsive in addition to attractive interactions, and which automatically determines an optimal number of clusters. Unfortunately, the multicut problem is NP-hard.
In response, we propose an objective function that allows for both repulsive and attractive interactions, but which can be solved to optimality by a greedy algorithm. At the time of writing, the new scheme gives the state of the art results on the ISBI connectomics challenge.
Joint work with Steffen Wolf, Constantin Pape, Nasim Rahaman, Alberto Bailoni, Anna Kreshuk, Ullrich Koethe.