@TechReport{Gronat-TR-2012-06,
  IS = { zkontrolovano 15 Jan 2013 },
  UPDATE  = { 2012-03-20 },
author =      {Gron{\'a}t, Petr },
supervisor =  {Pajdla, Tom{\'a}{\v s}},
title =       {Visual Localization},
institution = {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2012},
month =       {March},
day =         {27},
type =        {Research Report},
number =      {CTU--CMP--2012--06},
issn =        {1213-2365},
pages =       {25},
figures =     {8},
authorship =  {100},
psurl       = {[Gronat-TR-2012-06.pdf]},
project =     {SGS10/190/OHK3/2T/13},
annote =      { The goal of visual-based localization is to retrieve
  images from a large geotagged image database depicting the same
  place as a given query photograph. In this report we present system
  for building a geotagged dataset for place recognition utilizing
  publically available data from Google Streetview. Second part of
  this report investigates whether suppressing malicious features in
  the database images 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 17k street view images of Paris.  },
keywords =    {Visual based localization, geotagged dataset, learnig local distance function},
}