Abstract. Image partitioning, or segmentation without semantics, is
the task of decomposing an image into distinct segments; or equivalently,
the task of detecting closed contours in an image. Most prior work either
requires seeds, one per segment; or a threshold; or formulates the task
as an NP-hard signed graph partitioning problem. Here, we propose an
algorithm with empirically linearithmic complexity. Unlike seeded watershed, the algorithm can accommodate not only attractive but also
repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. The
algorithm itself, which we dub “Mutex Watershed”, is closely related to
a minimal spanning tree computation. It is deterministic and easy to
implement. When presented with short-range attractive and long-range
repulsive cues from a deep neural network, the Mutex Watershed gives
results that currently define the state-of-the-art in the competitive ISBI
2012 EM segmentation benchmark. These results are also better than
those obtained from other recently proposed clustering strategies operating on the very same network outputs