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| function [mu,mask]=kmeans(ima,k)
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%
% kmeans image segmentation
%
% Input:
% ima: grey color image
% k: Number of classes
% Output:
% mu: vector of class means
% mask: clasification image mask
%
%
%
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check image
ima=double(ima);
copy=ima; % make a copy
ima=ima(:); % vectorize ima
mi=min(ima); % deal with negative
ima=ima-mi+1; % and zero values
s=length(ima);
% create image histogram
m=max(ima)+1;
h=zeros(1,m);
hc=zeros(1,m);
for i=1:s
if(ima(i)>0) h(ima(i))=h(ima(i))+1;end;
end
ind=find(h);
hl=length(ind);
% initiate centroids
mu=(1:k)*m/(k+1);
% start process
while(true)
oldmu=mu;
% current classification
for i=1:hl
c=abs(ind(i)-mu);
cc=find(c==min(c));
hc(ind(i))=cc(1);
end
%recalculation of means
for i=1:k,
a=find(hc==i);
mu(i)=sum(a.*h(a))/sum(h(a));
end
if(mu==oldmu) break;end;
end
% calculate mask
s=size(copy);
mask=zeros(s);
for i=1:s(1),
for j=1:s(2),
c=abs(copy(i,j)-mu);
a=find(c==min(c));
mask(i,j)=a(1);
end
end
mu=mu+mi-1; % recover real range |
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