kmeans图像分类

clear %循环读取所有图片，得到灰度矩阵，分别铺成一行，放入data中，形成kmeans数据data pt = 'C:\matlab2017b\my-file\kmeans\image100\'; ext = '*.jpg'; dis = dir([pt ext]); nms = {dis.name}; data = zeros(length(nms),98304); for k = 1:1:length(nms) nm = [pt nms{k}]; I = imread(nm); gray = rgb2gray(I); data(k,:) = reshape(gray,1,98304); end %kmeans聚类算法处理数据 N=6;%设置聚类数目 [m,n]=size(data); pattern=zeros(m,n+1); center=zeros(N,n);%初始化聚类中心 pattern(:,1:n)=data(:,:); for x=1:N center(x,:)=data(randi(60,1),:);%第一次随机产生聚类中心 end while true distence=zeros(1,N); num=zeros(1,N); new_center=zeros(N,n); for x=1:m for y=1:N distence(y)=norm(data(x,:)-center(y,:));%计算到每个类的距离 end [~, temp]=min(distence);%求最小的距离 pattern(x,n+1)=temp; end k=0; for y=1:N for x=1:m if pattern(x,n+1)==y new_center(y,:)=new_center(y,:)+pattern(x,1:n); num(y)=num(y)+1; end end new_center(y,:)=new_center(y,:)/num(y); if norm(new_center(y,:)-center(y,:))<0.1 k=k+1; end end if k==N break; else center=new_center; end end [m, n]=size(pattern); %最后显示聚类后的灰度图像 % for i=1:m % if pattern(i,n)==1 % imshow(reshape(pattern(i,1:n-1),256,384)) % c1 = zeros(256,384); % c1(:,:) = reshape(pattern(i,1:n),256,384); % mkdir image1 % imwrite(gray,'image1/test1.png') % elseif pattern(i,n)==2 % plot(pattern(i,1),pattern(i,2),'g*'); % plot(center(2,1),center(2,2),'ko'); % elseif pattern(i,n)==3 % plot(pattern(i,1),pattern(i,2),'b*'); % plot(center(3,1),center(3,2),'ko'); % % elseif pattern(i,n)==4 % % plot(pattern(i,1),pattern(i,2),'y*'); % % plot(center(4,1),center(4,2),'ko'); % else % plot(pattern(i,1),pattern(i,2),'m*'); % plot(center(4,1),center(4,2),'ko'); % end % end c = zeros(256,384); for j = 1:1:N a = find(pattern(:,n)==j); a = a'; figure for i = 1:1:num(j) subplot(1,num(j),i) nm1 = [pt nms{a(i)}]; I1 = imread(nm1); imshow(I1) end end