  Hierarchical image segmentation provides region-oriented scalespace, i.e., a
set of image segmentations at different detail levels in which the
segmentations at finer levels are nested with respect to those at coarser
levels. Most image segmentation algorithms, such as region merging algorithms,
rely on a criterion for merging that does not lead to a hierarchy, and for
which the tuning of the parameters can be difficult. In this work, we propose a
hierarchical graph based image segmentation relying on a criterion popularized
by Felzenzwalb and Huttenlocher. We illustrate with both real and synthetic
images, showing efficiency, ease of use, and robustness of our method.
