@Article{Chum-PAMI-2010,
  IS = { zkontrolovano 27 Jul 2010 },
  UPDATE  = { 2010-07-27 },
  author =       {Chum, Ond{\v r}ej and Matas, Ji{\v r}{\' i}},
  title =        {Large Scale Discovery of Spatially Related Images},
  journal =      {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
  volume =       {32},
  number =       {2},
  year =         {2010},
  month =        {February},
  publisher =    {IEEE Computer Society},
  address =      {New York, USA},
  issn =         {0162-8828},
  pages =        {371--377},
  authorship =   {50-50},
  project =      {GACR 102/09/P423, MSM6840770038, ICT-215078 DIPLECS},
  keywords =     {image clustering, data mining, min-hash, image retrieval},
  annote = {We propose a randomized data mining method that finds
    clusters of spatially overlapping images. The core of the method
    relies on the min-Hash algorithm for fast detection of pairs of
    images with spatial overlap, the so-called cluster seeds.  The
    seeds are then used as visual queries to obtain clusters which are
    formed as transitive closures of sets of partially overlapping
    images that include the seed. We show that the probability of
    finding a seed for an image cluster rapidly increases with the
    size of the cluster.},
}