@InProceedings{grady2004:faster,
  author =	 {Leo Grady and Eric L. Schwartz},
  title =	 {Faster graph-theoretic image processing via
                  small-world and quadtree topologies},
  booktitle =	 {Proceedings of the 2004 IEEE Computer Society
                  Conference on Computer Vision and Pattern
                  Recognition},
  pages =	 {360--365},
  volume =	 2,
  series =	 {Conference on Computer Vision and Pattern
                  Recognition},
  year =	 2004,
  address =	 {Washington, DC},
  month =	 {June 27 -- July 2},
  organization = {IEEE Computer Society},
  publisher =	 {IEEE},
  abstract =	 {Numerical methods associated with graph-theoretic
                  image processing algorithms often reduce to the
                  solution of a large linear system. We show here that
                  choosing a topology that yields a small graph
                  diameter can greatly speed up the numerical
                  solution. As a proof of concept, we examine two
                  image graphs that preserve local connectivity of the
                  nodes (pixels) while drastically reducing the graph
                  diameter. The first is based on a ``small-world''
                  modification of a standard 4-connected lattice. The
                  second is based on a quadtree graph. Using a
                  recently described graph-theoretic image processing
                  algorithm we show that large speed-up is achieved
                  with a minimal perturbation of the solution when
                  these graph topologies are utilized. We suggest that
                  a variety of similar algorithms may also benefit
                  from this approach.},
}