快速K-均值(kmeans)聚类图像分割算法源代码

function [mu,mask]=kmeans(ima,k) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % kmeans image segmentation % % Input: % ima: grey color image % k: Number of classes % Output: % mu: vector of class means % mask: clasification image mask % % Author: Jose Vicente Manjon Herrera % Email: jmanjon@fis.upv.es % Date: 27-08-2005 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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