Hierarchical Image Partitioning with Dual Graph Contraction (bibtex)
by Yll Haxhimusa, Walter G. Kropatsch
Abstract:
We present a hierarchical partitioning of images using a pairwise similarity function on a graph-based representation of an image. This function measures the difference along the boundary of two components relative to a measure of differences of the components internal differences. This definition tries to encapsulate the intuitive notion of contrast. Two components are merged if there is a low-cost connection between them. Each component's internal difference is represented by the maximum edge weight of its minimum spanning tree. External differences are the smallest weight of edges connecting components. We use this idea for building a minimum spanning tree to find region borders quickly and effortlessly in a bottom-up way, based on local differences in a specific feature
Reference:
Hierarchical Image Partitioning with Dual Graph Contraction (Yll Haxhimusa, Walter G. Kropatsch), Technical report, PRIP, TU Wien, 2003.
Bibtex Entry:
@TechReport{PTR-Haxhimusa03b,
  author =	 "Yll Haxhimusa and Walter G. Kropatsch",
  title =	 "Hierarchical {I}mage {P}artitioning with {D}ual
                  {G}raph {C}ontraction",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-081",
  year =	 "2003",
  url =		 "ftp://ftp.prip.tuwien.ac.at/pub/publications/trs/tr81.pdf",
  abstract =	 " We present a hierarchical partitioning of images
                  using a pairwise similarity function on a
                  graph-based representation of an image. This
                  function measures the difference along the boundary
                  of two components relative to a measure of
                  differences of the components internal
                  differences. This definition tries to encapsulate
                  the intuitive notion of contrast. Two components are
                  merged if there is a low-cost connection between
                  them. Each component's internal difference is
                  represented by the maximum edge weight of its
                  minimum spanning tree. External differences are the
                  smallest weight of edges connecting components. We
                  use this idea for building a minimum spanning tree
                  to find region borders quickly and effortlessly in a
                  bottom-up way, based on local differences in a
                  specific feature ",
}
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