Hierarchical Segmentation Using Tree-Based Shape Spaces

From LRDE

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.

Documents

Bibtex (lrde.bib)

@Article{	  xu.16.pami,
  author	= {Yongchao Xu and Edwin Carlinet and Thierry G\'eraud and
		  Laurent Najman},
  title		= {Hierarchical Segmentation Using Tree-Based Shape Spaces},
  journal	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence},
  year		= {2017},
  volume	= {39},
  number	= {3},
  pages		= {457--469},
  month		= apr,
  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.}
}