  This paper presents a new probabilistic generative model for image
segmentation, i.e. the task of partitioning an image into homogeneous regions.
Our model is grounded on a mid-level image representation, called a region
tree, in which regions are recursively split into subregions until superpixels
are reached. Given the region tree, image segmentation is formalized as
sampling cuts in the tree from the model. Inference for the cuts is exact, and
formulated using dynamic programming. Our tree-cut model can be tuned to sample
segmentations at a particular scale of interest out of many possible multiscale
image segmentations. This generalizes the common notion that there should be
only one correct segmentation per image. Also, it allows moving beyond the
standard single-scale evaluation, where the segmentation result for an image is
averaged against the corresponding set of coarse and fine human annotations, to
conduct a scale-specific evaluation. Our quantitative results are comparable to
those of the leading gPb-owt-ucm method, with the notable advantage that we
additionally produce a distribution over all possible tree-consistent
segmentations of the image.
