  Image segmentation is the process of partitioning an image into a set of
meaningful regions according to some criteria. Hierarchical segmentation has
emerged as a major trend in this regard as it favors the emergence of important
regions at different scales. On the other hand, many methods allow us to have
prior information on the position of structures of interest in the images. In
this paper, we present a versatile hierarchical segmentation method that takes
into account any prior spatial information and outputs a hierarchical
segmentation that emphasizes the contours or regions of interest while
preserving the important structures in the image. An application of this method
to the weakly-supervised segmentation problem is presented.
