@Article{grady2006:isoperimetric_a,
  author =       {Leo Grady and Eric L. Schwartz},
  title =        {Isoperimetric Graph Partitioning for Data Clustering
                  and Image Segmentation},
  journal =      {IEEE Pattern Analysis and Machine Intelligence},
  volume =       28,
  number =       3,
  pages =        {469--475},
  year =         2006,
  abstract =     {Spectral graph partitioning provides a powerful
                  approach to image segmentation. We introduce an
                  alternate idea that finds partitions with a small
                  isoperimetric constant, requiring solution to a
                  linear system rather than an eigenvector problem.
                  This approach produces the high quality
                  segmentations of spectral methods, but with improved
                  speed and stability.},
  keywords =     {Graph-theoretic methods, graphs and networks, graph
                  algorithms, image representation, special
                  architectures, algorithms, computer vision,
                  applications},
  DOI =
                  {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.57},
  url =
                  {http://csdl.computer.org/dl/trans/tp/2006/03/i0469.htm},
  datestr =      200602,
}