@InProceedings{GronatCVWW2012,
  IS = { zkontrolovano 14 Jan 2013 },
  UPDATE  = { 2012-03-20 },
  author =  { Gron{\'a}t, Petr and {\v S}ivic, Josef and Pajdla, Tom{\'a}{\v s}},
  title =       {Learning local distance functions for place recognition},
  year =        {2012},
  pages =       {168-173},
  booktitle =   {CVWW '12: Proceedings of the 17th Computer Vision Winter Workshop},
  editor =      {Matej Kristan and Luka \v{C}ehovin and Rok Mandeljc},
  publisher =   {Slovenian Pattern Recognition Society},
  address =     {Ljubljana, Slovenia},
  isbn =        {978-961-90901-6-9},
  book_pages =  {181},
  month =       {February},
  day =         {1-3},
  venue =       {Mala Nedelja, Slovenia},
  annote =      {The goal of this work is to retrieve images from a
    large geotagged image database depicting the same place as a given
    query photograph. Previous work has shown that objects, which
    frequently occur in the database (such as trees or road mark
    ings), can cause significant confusion between different places,
    and suppressing features on such "confusing" objects can
    significantly improve retrieval performance. In this work, we
    investigate whether suppressing confusing features in the database
    can be cast as learning local per-image distance functions. We
    demonstrate that finding such local distance functions can be
    formulated as a linear program and perform initial experiments on
    a database of 17,000 street view images of Paris.},
  keywords =    {place recognition, local distance function learning},
  prestige =    {international},
  authorship =  {34-33-33},
  project =     {SGS10/190/OHK3/2T/13, FP7-SPACE-241523 PRoViScout},
  psurl = {[Gronat-Sivic-Pajdla-CVWW-2012]},
  www = {http://cvww2012.vicos.si/},
  acceptance_ratio = {0.9},
}