IS = { zkontrolovano 18 Jan 2009 },
  UPDATE  = { 2008-12-16 },
  author =      {Cai, Hongping  and Mikolajczyk, Krystian and
                 Matas, Ji{\v r}{\' \i}},
  title =       {Learning Linear Discriminant Projections for
  Dimensionality Reduction of Image Descriptors},
  authorship =  {50-30-20},
  year =        {2008},
  pages =       {503--512},
  booktitle =   {BMVC 2008: Proceedings of the 19th British
                 Machine Vision Conference},
  volume =      {1},
  editor =      {Everingham, M. and Needham, C.  and Fraille, R.},
  isbn =        {978-1-901725-36-0},
  book_pages =  {1194},
  publisher =   {BMVA},
  address =     {London, UK},
  month =       {September},
  day =         {1--4},
  venue =       {Leeds, UK},
  project =     {MSM6840770038},
  psurl =       {pdf},
  annote = {This paper proposes a general method for improving image
    descriptors using discriminant projections. Two methods based on
    Linear Discriminant Analysis have been recently introduced to
    improve matching performance of local descriptors and to reduce
    their dimensionality. These methods require large training set
    with ground truth of accurate point-to-point correspondences which
    limits their applicability. We demonstrate the theoretical
    equivalence of these methods and provide a means to derive
    projection vectors on data without available ground truth. It
    makes it possible to apply this technique and improve performance
    of any combination of interest point detectors-descriptors. We
    conduct an extensive evaluation of the discriminative projection
    methods in various application scenarios.  The results validate
    the proposed method in viewpoint invariant matching and category
  keywords =    {computer vision, linear discrimant analysis, SIFT},