12 Apr
2016
12 Apr
'16
2:58 p.m.
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.