2DPCA代码_2DPCA_

dglobal samplespath;%图片位置 global Y; global x; global train_num; global row; global column; global accuracies; row=112;%行 column=92;%列 train_num=200; accuracies=zeros(1,column); Gt=zeros(column); samplespath='E:\ORL\s'; for i=1:40 suma=zeros(row,column); for j=1:5 a=imread(strcat(samplespath,num2str(i),'\',num2str(j),'.pgm'));%strcat()是连接字符串的函数 a=double(a); suma=suma+a; end avrageA=suma/5; for j=1:5 a=imread(strcat(samplespath,num2str(i),'\',num2str(j),'.pgm')); a=double(a); Gt=Gt+(a-avrageA)'*(a-avrageA); end end Gt=Gt/train_num; [v, d]=eig(Gt);%求特征值和特征向量，其中特征值在d的对角线上，d为协方差矩阵 cols=size(v,2);%矩阵列数 dd=zeros(1,column);%存放特征值 for i=1:column dd(i)=d(i,i);%用一个向量存放特征值 end [d2,index]=sort(dd,'descend'); dsort=zeros(column); dsort1=zeros(column); for i=1:cols dsort(:,i)=v(:,index(i));%根据特征值降序插入到dsort中去 end dsum=sum(dd);%协方差矩阵的迹 dsum_extract=0; for dim=1:column x=dsort(:,1:dim);%x向量矩阵 Y=cell(1,40);%存放40组图片前五张的样本平均值 for i=1:40 tempA=zeros(row,column); for j=1:5 a=imread(strcat(samplespath,num2str(i),'\',num2str(j),'.pgm')); a=double(a); tempA=tempA+a; end tempA=tempA/5; Y(i)=mat2cell(tempA*x,112,dim); end %测试，最近邻分类器 accu=0; for i=1:40 for j=6:10%测试样本 a=imread(strcat(samplespath,num2str(i),'\',num2str(j),'.pgm')); a=double(a); tempY=a*x;%当前图片的特征向量矩阵 tempindex=1;%存放当前图片 tempsum=10000000;%存放测试图片与测试样本的最小值 for k=1:40 sumlast=0.0; YY=cell2mat(Y(k)); for l=1:dim sumlast=sumlast+norm(tempY(:,l)-YY(:,l)); % sumlast=norm(tempY-YY); end if(tempsum>sumlast) tempsum=sumlast; tempindex=k;%分类 end end %判断分类是否正确 if tempindex==i accu=accu+1; end end end accuracy=accu/200; accuracies(dim)=accuracy; end %绘图 dimensions=zeros(1,column); for di=1:column dimensions(di)=di; end plot(dimensions,accuracies);