  This paper investigates one of the most fundamental computer vision problems:
image segmentation. We propose a supervised hierarchical approach to
object-independent image segmentation. Starting with over-segmenting
superpixels, we use a tree structure to represent the hierarchy of region
merging, by which we reduce the problem of segmenting image regions to finding
a set of label assignment to tree nodes. We formulate the tree structure as a
constrained conditional model to associate region merging with likelihoods
predicted using an ensemble boundary classifier. Final segmentations can then
be inferred by finding globally optimal solutions to the model efficiently. We
also present an iterative training and testing algorithm that generates various
tree structures and combines them to emphasize accurate boundaries by
segmentation accumulation. Experiment results and comparisons with other very
recent methods on six public data sets demonstrate that our approach achieves
the state-of-the-art region accuracy and is very competitive in image
segmentation without semantic priors.

Computer Vision and Pattern Recognition