Difference between revisions of "Publications/xu.16.pami"

From LRDE

(Created page with "{{Publication | published = true | date = 2016-04-11 | authors = Yongchao Xu, Edwin Carlinet, Thierry Géraud, Laurent Najman | title = Hierarchical Segmentation Using Tree-Ba...")
 
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| project = Image
 
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| 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 paperwe 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.
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| 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.
 
| urllrde = 201604-PAMI
 
| urllrde = 201604-PAMI
 
| note = To appear
 
| note = To appear
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   number = <nowiki>{</nowiki>99<nowiki>}</nowiki>,
 
   number = <nowiki>{</nowiki>99<nowiki>}</nowiki>,
 
   pages = <nowiki>{</nowiki>1-14<nowiki>}</nowiki>,
 
   pages = <nowiki>{</nowiki>1-14<nowiki>}</nowiki>,
  month = <nowiki>{</nowiki><nowiki>}</nowiki>,
 
 
   project = <nowiki>{</nowiki>Image<nowiki>}</nowiki>,
 
   project = <nowiki>{</nowiki>Image<nowiki>}</nowiki>,
 
   abstract = <nowiki>{</nowiki>Current trends in image segmentation are to compute a
 
   abstract = <nowiki>{</nowiki>Current trends in image segmentation are to compute a

Revision as of 19:59, 8 April 2016

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		= {2016},
  volume	= {PP},
  number	= {99},
  pages		= {1-14},
  project	= {Image},
  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.},
  note		= {To appear}
}