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📄 hikmeanspush.m

📁 这是图像识别方法bag of feature 的matlab源代码
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function asgn = hikmeanspush(tree,data)% HIKMEANSPUSH   Push data down an integer K-means tree%   ASGN=HIKMEANSPUSH(TREE,DATA) assigns data point DATA to%   nodes of the hierachical K-means tree TREE.%   See also HIKMEANS().%% AUTORIGHTS% Copyright (C) 2006 Regents of the University of California% All rights reserved% % Written by Andrea Vedaldi (UCLA VisionLab).% % Redistribution and use in source and binary forms, with or without% modification, are permitted provided that the following conditions are met% %     * Redistributions of source code must retain the above copyright%       notice, this list of conditions and the following disclaimer.%     * Redistributions in binary form must reproduce the above copyright%       notice, this list of conditions and the following disclaimer in the%       documentation and/or other materials provided with the distribution.%     * Neither the name of the University of California, Berkeley nor the%       names of its contributors may be used to endorse or promote products%       derived from this software without specific prior written permission.% % THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY% EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED% WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE% DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANY% DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES% (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;% LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND% ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT% (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS% SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.asgn = xpush(tree,data,tree.depth) ;% --------------------------------------------------------------------function asgn = xpush(tree,data,depth)% --------------------------------------------------------------------% Recusrively partition dataN    = size(data,2) ;M    = size(data,1) ;asgn = uint32(zeros(depth, N)); if(~isstruct(tree)), return ; endthis_asgn = ikmeanspush(data,tree.centers) ;asgn(1,:) = this_asgn ;% recursively descend in each subtreeif depth > 1  K_eff = size(tree.centers,2) ;  for k=1:K_eff      sel=find(asgn(1,:)==k) ;    if(isempty(sel)), continue ; end    sub_asgn = xpush(tree.sub(k),data(:,sel),depth-1) ;  	asgn(2:end,sel) = sub_asgn ;  endend

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