@Article{cai-msift-pami2011,
  IS = { zkontrolovano 24 Jan 2011 },
  UPDATE  = { 2011-01-14 },
  author =  {Cau, Hongping and
             Mikolajczyk, Krystian and
             Matas, Ji{\v r}{\' i}},
  title =   {Learning Linear Discriminant Projections for Dimensionality
             Reduction of Image Descriptors},
  journal =      {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
  volume =       {33},
  number =       {2},
  year =         {2011},
  month =        {February},
  publisher =    {IEEE Computer Society},
  address =      {Los Alamitos, USA},
  issn =         {0162-8828},
  pages =        {338-352},
  authorship =   {34-33-33},
  project =      { MSM6840770038 },
  keywords =     { Linear discriminant projections, dimensionality
                  reduction, image descriptors, image recognition, 
                  image matching},
  psurl = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.89},
  annote = { In this paper, we present Linear Discriminant Projections
   (LDP) for reducing dimensionality and improving discriminability of
   local image descriptors. We place LDP into the context of
   state-of-the-art discriminant projections and analyze its
   properties. LDP requires a large set of training data with
   point-to-point correspondence ground truth. We demonstrate that
   training data produced by a simulation of image transformations
   leads to nearly the same results as the real data with
   correspondence ground truth. This makes it possible to apply LDP as
   well as other discriminant projection approaches to the problems
   where the correspondence ground truth is not available, such as
   image categorization. We perform an extensive experimental
   evaluation on standard data sets in the context of image matching
   and categorization. We demonstrate that LDP enables significant
   dimensionality reduction of local descriptors and performance
   increases in different applications. The results improve upon the
   state-of-the-art recognition performance with simultaneous
   dimensionality reduction from 128 to 30.},
}