@InProceedings{ToriiSivicPajdlaWMV11,
  IS = { zkontrolovano 10 Jan 2012 },
  UPDATE  = { 2011-12-29 },
  author =      {Torii, Akihiko and Sivic, Josef and Pajdla, Tom{\'a}{\v s}},
  title =       {Visual localization by linear combination of image descriptors},
  year =        {2011},
  pages =       {102-109},
  book_pages =  {2204},
  isbn =        {978-1-4673-0063-6},
  booktitle =   {2011 IEEE International Conference on Computer
                 Vision Workshops (ICCV Workshops)},
  conference =  {The Second IEEE International Workshop on Mobile Vision},
  editor =      {Hua, Gang and Fu, Yun and Turk, Matthew and Pulli, Kari},
  publisher =   {IEEE Computer Society},
  address =     {Los Alamitos, USA},
  month =	{November},
  day =		{7},
  venue =	{Barcelona, Spain},
  annote = 	{We seek to predict the GPS location of a query image
    given a database of images localized on a map with known GPS
    locations. The contributions of this work are three-fold: (1) we
    formulate the image-based localization problem as a regression on
    an image graph with images as nodes and edges connecting close-by
    images; (2) we design a novel image matching procedure, which
    computes similarity between the query and pairs of database images
    using edges of the graph and considering linear combinations of
    their feature vectors.  This improves generalization to unseen
    viewpoints and illumination conditions, while reducing the
    database size; (3) we demonstrate that the query location can be
    predicted by interpolating locations of matched images in the
    graph without the costly estimation of multi-view geometry.  We
    demonstrate benefits of the proposed image matching scheme on the
    standard Oxford building benchmark, and show localization results
    on a database of 8,999 panoramic Google Street View images of
    Pittsburgh.},
  keywords =	{image search, mage based localization},
  project =	{SGS10/190/OHK3/2T/13, WILLOW project, 
                 Laboratoire d'Informatique de l'Ecole Normale Superieure, 
                 ENS/INRIA/CNRS UMR 8548},
  authorship =  {25-25-50},
  note =        {CD-ROM},
}