We are pleased to announce that the following paper has been accepted for publication in the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). Find the title and abstract below. Title and corresponding authors: Hierarchical Segmentation Using Tree-Based Shape Spaces Yongchao Xu(13), Edwin Carlinet(1), Thierry Geraud(1), Laurent Najman(2) (1) LRDE, EPITA, Kremlin-Bicêtre, France (2) LIGM, Équipe A3SI, ESIEE Paris, 93160 Noisy-le-Grand, France (3) LTCI, CNRS, Téléecom ParisTech, 75013, Paris, France Abstract: Current trends in image segmentation are to compute a hierarchy of image segmentations from fine to coarse. A classical approach to obtain a single meaningful image partition from a given hierarchy is to cut it in an optimal way, following the seminal approach of the scale-set theory. While interesting in many cases, the resulting segmentation, being a non-horizontal cut, is limited by the structure of the hierarchy. In this paper, we propose a novel approach that acts by transforming an input hierarchy into a new saliency map. It relies on the notion of shape space: a graph representation of a set of regions extracted from the image. Each region is characterized with an attribute describing it. We weigh the boundaries of a subset of meaningful regions (local minima) in the shape space by extinction values based on the attribute. This extinction-based saliency map represents a new hierarchy of segmentations highlighting regions having some specific characteristics. Each threshold of this map represents a segmentation which is generally different from any cut of the original hierarchy. This new approach thus enlarges the set of possible partition results that can be extracted from a given hierarchy. Qualitative and quantitative illustrations demonstrate the usefulness of the proposed method.