IS = { zkontrolovano 21 Jan 2009 },
  UPDATE  = { 2008-12-29 },
  author =	 {Omer{\v c}evi{\'c}, Du{\v s}an and Drbohlav, Ond{\v
                  r}ej and Leonardis, Ale{\v s}},
  title =	 {High-Dimensional Feature Matching: Employing the
                  Concept of Meaningful Nearest Neighbors},
  year =	 {2007},
  pages =	 {8},
  booktitle =	 {ICCV 2007: Proceedings of Eleventh IEEE
                  International Conference on Computer Vision},
  editor =	 {Dimitris Metaxas and Baba Vemuri and Amnon Shashua
                  and Harry Shum},
  publisher =	 {IEEE Computer Society Press},
  address =	 {Los Alamitos, USA},
  isbn =	 {978-1-4244-1631-8},
  book_pages =	 {2240},
  month =	 {October},
  day =		 {14-20},
  venue =	 {Rio de Janeiro, Brazil},
  organization = {IEEE Computer Society},
  annote = { Matching of high-dimensional features using nearest
    neighbors search is an important part of image matching methods
    which are based on local invariant features. In this work we
    highlight effects pertinent to high-dimensional spaces that are
    significant for matching, yet have not been explicitly accounted
    for in previous work. In our approach, we require every nearest
    neighbor to be meaningful, that is, sufficiently close to a query
    feature such that it is an outlier to a background feature
    distribution. We estimate the background feature distribution from
    the extended neighborhood of a query feature given by its k
    nearest neighbors.  Based on the concept of meaningful nearest
    neighbors, we develop a novel high-dimensional feature matching
    method and evaluate its performance by conducting image matching
    on two challenging image data sets. A superior performance in
    terms of accuracy is shown in comparison to several
    state-of-the-art approaches. Additionally, to make search for k
    nearest neighbors more efficient, we develop a novel approximate
    nearest neighbors search method based on sparse coding with an
    overcomplete basis set that provides a ten-fold speed-up over an
    exhaustive search even for high dimensional spaces and retains
    excellent approximation to an exact nearest neighbors search.  },