Documentation>vl_demo_kdtree_ann

# Source code for ``vl_demo_kdtree_ann.m

This file is located in the ``toolbox/demo folder in VLFeat package.

    function vl_demo_kdtree_ann
    % VL_DEMO_KDTREE
    %   Demonstrates the use of a kd-tree for approximate nearest neighbor
    %   (ANN) queries.

    % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
    % All rights reserved.
    %
    % This file is part of the VLFeat library and is made available under
    % the terms of the BSD license (see the COPYING file).

    randn('state',0) ;
    rand('state',0) ;

    % Generate some 2D data and a query point
    X = rand(2, 100) ;
    Q = rand(2,1) ;

    % Buld a kd-tree
    kdtree = vl_kdtreebuild(X) ;

    % Query with increasing accuracy
    maxNumComparisonRange = [1 10 20 30] ;
    for t = [1 2 3 4]
      figure(t) ; clf ;
      vl_plotframe(X, 'ro') ;
      hold on ;
      xl = [.2, .8] ;
      yl = [.1, .7] ;
      xlim(xl) ;
      ylim(yl) ;

      %  vl_demo_kdtree_plot(kdtree, 1, xl, yl) ;

      [i, d] = vl_kdtreequery (kdtree, X, Q, ...
                               'NumNeighbors', 10, ...
                               'MaxComparisons', maxNumComparisonRange(t), ...
                               'Verbose') ;

      vl_plotframe(Q,'b*','markersize',10) ;
      for k=1:length(i)
        if i(k) == 0, continue ; end
        vl_plotframe([Q ; sqrt(d(k))],'b-','linewidth',1) ;
        vl_plotframe(X(:, i(k)), 'bx','markersize',15) ;
      end
      title(sprintf('10 ANNs with at most %d comparisions', maxNumComparisonRange(t))) ;

      axis square ;
      set(gca,'xtick',[],'ytick',[]) ;
      vl_demo_print(t, sprintf('kdtree_ann_%d', t)) ;
    end