IS = { zkontrolovano 02 Jan 2006 },
  UPDATE  = { 2005-10-03 },
  author =      {Obdr{\v z}{\' a}lek, {\v S}t{\v e}p{\' a}n and  
                 Matas, Ji{\v r}{\' \i}},
  title =       {Sub-linear Indexing for Large Scale Object Recognition},
  authorship =  {50-50},
  year =        {2005},
  pages =       {1--10},
  booktitle =   {BMVC 2005: Proceedings of the 16th British Machine Vision Conference},
  volume =      {1},
  editor =      {Clocksin, WF and Fitzgibbon, AW and Torr, PHS},
  isbn =        {1-901725-29-4},
  book_pages =  {951},
  publisher =   {BMVA},
  address =     {London, UK},
  month =       {September},
  day =         {5--8},
  venue =       {Oxford, UK},
  project =     {GACR 102/03/0440, COSPAL IST-004176},
  psurl =       {pdf},
  annote={ Realistic approaches to large scale object recognition,
    i.e. for detection and localisation of hundreds or more objects,
    must support sub-linear time indexing. In the paper, we propose a
    method capable of recognising one of N objects in log(N) time. The
    .visual memory.  is organised as a binary decision tree that is
    built to minimise average time to decision. Leaves of the tree
    represent a few local image areas, and each non-terminal node is
    associated with a .weak classifier.. In the recognition phase, a
    single invariant measurement decides in which subtree a
    corresponding image area is sought. The method preserves all the
    strengths of local affine region methods . robustness to
    background clutter, occlusion, and large changes of
    viewpoints. Experimentally we show that it supports near real-time
    recognition of hundreds of objects with state-of-the-art
    recognition rates. After the test image is processed (in a second
    on a current PCs), the recognition via indexing into the visual
    memory requires milliseconds. },
 keywords =    {object recognition, local affine frames, MSER, LAF},