matlab下利用K-Means进行图像分类
FIRST & BEST SOLUTION
clear all; clc; I_rgb=imread(\'dog.jpg\'); figure();imshow(I_rgb);title(\'原始图像\'); %去噪 filter=ones(5,5); filter=filter/sum(filter(:)); denoised_r=conv2(I_rgb(:,:,1),filter,\'same\'); denoised_g=conv2(I_rgb(:,:,2),filter,\'same\'); denoised_b=conv2(I_rgb(:,:,3),filter,\'same\'); denoised_rgb=cat(3, denoised_r, denoised_g, denoised_b); D_rgb=uint8(denoised_rgb); figure();imshow(D_rgb);title(\'去噪后图像\');%去噪后的结果 %将彩色图像从RGB转化到lab彩色空间 C =makecform(\'srgb2lab\'); %设置转换格式 I_lab= applycform(D_rgb, C); %进行K-mean聚类将图像分割成2个区域 ab =double(I_lab(:,:,2:3)); %取出lab空间的a分量和b分量 nrows= size(ab,1); ncols= size(ab,2); ab =reshape(ab,nrows*ncols,2); nColors= 4; %分割的区域个数为4 [cluster_idx,cluster_center] =kmeans(ab,nColors,\'distance\',\'sqEuclidean\',\'Replicates\',2); %重复聚类2次 pixel_labels= reshape(cluster_idx,nrows,ncols); %显示分割后的各个区域 segmented_images= cell(1,4); rgb_label= repmat(pixel_labels,[1 1 3]); for k= 1:nColors color = I_rgb; color(rgb_label ~= k) = 0; segmented_images{k} = color; end figure(),imshow(segmented_images{1}),title(\'分割结果——区域1\'); figure(),imshow(segmented_images{2}),title(\'分割结果——区域2\'); figure(),imshow(segmented_images{3}),title(\'分割结果——区域3\'); figure(),imshow(segmented_images{4}),title(\'分割结果——区域4\');%使分割后的图像在一个图中显示 m=uint8(rgb_label); for i=1:69 for j=1:97 if m(i,j,1)==1 m(i,j,1)=255; m(i,j,2)=0; m(i,j,3)=0; end if m(i,j,1)==2 m(i,j,1)=256; m(i,j,2)=256; m(i,j,3)=0; end if m(i,j,1)==3 m(i,j,1)=0; m(i,j,2)=0; m(i,j,3)=255; end if m(i,j,1)==4 m(i,j,1)=0; m(i,j,2)=128; m(i,j,3)=0; end end end figure(),imshow(m)
将 调用k-means算法的那句更换成下面代码，自己实现k-means
cluster_idx=zeros(6693,1); ct11=90; ct12=90; ct21=110; ct22=110; ct31=130; ct32=130; ct41=150; ct42=150; %初始分类 sum1=[0,0]; sum2=[0,0]; sum3=[0,0]; sum4=[0,0];
f1=0;
f2=0;
f3=0;
f4=0;
for k=1:20 for i=1:6693 d1=(ab(i,1)-ct11).^2+(ab(i,2)-ct12).^2; d2=(ab(i,1)-ct21).^2+(ab(i,2)-ct22).^2; d3=(ab(i,1)-ct31).^2+(ab(i,2)-ct32).^2; d4=(ab(i,1)-ct41).^2+(ab(i,2)-ct42).^2;
Z=[d1,d2,d3,d4]; m=min(Z); if m==d1 cluster_idx(i)=1;
f1=1+f1; sum1=sum1+ab(i,:); end if m==d2 cluster_idx(i)=2;
f2=f2+1; sum2=sum2+ab(i,:); end if m==d3 cluster_idx(i)=3;
f3=f3+1; sum3=sum3+ab(i,:); end if m==d4 cluster_idx(i)=4;
f4=f4+1; sum4=sum4+ab(i,:); end end ct11=sum1(1,1)/f1; ct12=sum1(1,2)/f1; ct21=sum2(1,1)/f2; ct22=sum2(1,2)/f2; ct31=sum3(1,1)/f3; ct32=sum3(1,2)/f3; ct41=sum4(1,1)/f4; ct42=sum4(1,2)/f4; end ct1=[ct11,ct12]; ct2=[ct21,ct22]; ct3=[ct31,ct32]; ct4=[ct41,ct42];
ANOTHER SOLUTION
RGB= imread (\'dog.jpg\'); %读入图像 [m n]=size(RGB); %m是数据个数，n是数据维度 figure(),imshow(RGB);title(\' 图一 彩色原图像\') hold off; RGB=double(RGB); filter=ones(5,5); filter=filter/sum(filter(:)); denoised_r=conv2(RGB(:,:,1),filter,\'same\'); denoised_g=conv2(RGB(:,:,2),filter,\'same\'); denoised_b=conv2(RGB(:,:,3),filter,\'same\'); denoised_rgb=cat(3, denoised_r, denoised_g, denoised_b); RGB=uint8(denoised_rgb); figure();imshow(RGB);title(\'去噪后图像\');%去噪后的结果 RGB=double(RGB); img1= RGB(:,:,1); img2=RGB (:,:,2); img3= RGB (:,:,3); t=0; c11(1)=4; c12(1)=4; c13(1)=4; c21(1)=70; c22(1)=67; c23(1)=71; c31(1)=100; c32(1)=100; c33(1)=100; c41(1)=200; c42(1)=200; c43(1)=200;%选四个初始聚类中心 cluster_idx=zeros(69,97); class1_num=0; class2_num=0; class3_num=0; class4_num=0; sum_class11=0; sum_class21=0; sum_class31=0; sum_class41=0; sum_class12=0; sum_class22=0; sum_class32=0; sum_class42=0; sum_class13=0; sum_class23=0; sum_class33=0; sum_class43=0; for k=1:20 if t==0 for i=1:69 for j=1:97 r=sqrt((img1(i,j)-c11(k))^2+(img2(i,j)-c12(k))^2+(img3(i,j)-c13(k))^2); g=sqrt((img1(i,j)-c21(k))^2+(img2(i,j)-c22(k))^2+(img3(i,j)-c23(k))^2); b=sqrt((img1(i,j)-c31(k))^2+(img2(i,j)-c32(k))^2+(img3(i,j)-c33(k))^2); q=sqrt((img1(i,j)-c41(k))^2+(img2(i,j)-c42(k))^2+(img3(i,j)-c43(k))^2); %计算各像素灰度与聚类中心的距离 Z=[r,g,b,q]; d=min(Z); if d==r class1_num=class1_num+1; cluster_idx(i,j)=1; sum_class11=sum_class11+img1(i,j); sum_class12=sum_class12+img2(i,j); sum_class13=sum_class13+img3(i,j); end if d==g class2_num=class2_num+1; cluster_idx(i,j)=2; sum_class21=sum_class21+img1(i,j); sum_class22=sum_class22+img2(i,j); sum_class23=sum_class23+img3(i,j); end if d==b class3_num=class3_num+1; cluster_idx(i,j)=3; sum_class31=sum_class31+img1(i,j); sum_class32=sum_class32+img2(i,j); sum_class33=sum_class33+img3(i,j); end if d==q class4_num=class4_num+1; cluster_idx(i,j)=4; sum_class41=sum_class41+img1(i,j); sum_class42=sum_class42+img2(i,j); sum_class43=sum_class43+img3(i,j); end end end c11(k+1)=sum_class11/class1_num; c21(k+1)=sum_class21/class2_num; c31(k+1)=sum_class31/class3_num; c41(k+1)=sum_class41/class4_num;%将所有低灰度求和取平均，作为下一个低灰度中心 c12(k+1)=sum_class12/class1_num; c22(k+1)=sum_class22/class2_num; c42(k+1)=sum_class42/class4_num; c32(k+1)=sum_class32/class3_num;%将所有低灰度求和取平均，作为下一个中间灰度中心 c13(k+1)=sum_class13/class1_num; c23(k+1)=sum_class23/class2_num; c43(k+1)=sum_class43/class4_num; c33(k+1)=sum_class33/class3_num;%将所有低灰度求和取平均，作为下一个高灰度中心 d11=abs(c11(k+1)-c11(k)); d12=abs(c12(k+1)-c12(k)); d13=abs(c13(k+1)-c13(k)); d21=abs(c21(k+1)-c21(k)); d22=abs(c22(k+1)-c22(k)); d23=abs(c23(k+1)-c23(k)); d31=abs(c31(k+1)-c31(k)); d32=abs(c32(k+1)-c32(k)); d33=abs(c33(k+1)-c33(k)); d41=abs(c41(k+1)-c41(k)); d42=abs(c42(k+1)-c42(k)); d43=abs(c43(k+1)-c43(k)); if(d11<=0.001&&d12<=0.001&&d13<=0.001&&d21<=0.001&&d22<=0.001&&d23<=0.001&&d31<=0.001&&d32<=0.001&&d33<=0.001&&d41<=0.001&&d42<=0.001&&d43(k)<=0.001) t=1; end end end for i=1:69 for j=1:97 if cluster_idx(i,j)==1 img1(i,j)=255; img2(i,j)=0; img3(i,j)=0; end if cluster_idx(i,j)==2 img1(i,j)=256; img2(i,j)=256; img3(i,j)=0; end if cluster_idx(i,j)==3 img1(i,j)=0; img2(i,j)=0; img3(i,j)=255; end if cluster_idx(i,j)==4 img1(i,j)=0; img2(i,j)=128; img3(i,j)=0; end end end Img1=uint8(img1); Img2=uint8(img2); Img3=uint8(img3); R=cat(3,Img1,Img2,Img3); figure(),imshow(R);title(\'图二 聚类后的图像\')
