How to use kmeans clustering for face images present in database using eigenfaces features in matlab?

Question

prashanth 2014-3-28

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/123481-how-to-use-kmeans-clustering-for-face-images-present-in-database-using-eigenfaces-features-in-matlab

My code uses eigenfaces features to recognize faces.Iam having 20 images in my database.I have to make 3 clusters.Iam using kmeans clustering method.This code can calculate maximum and minimum eucledian distance for each input image taken from database using weight vector.Using this feature i have to make cluster.In matlab software kmeans command is available IDX=kmeans(data,k).I took minimum euclidean distance as 'data' and k=3.It is making minimum euclidean distance of input image as 3 clusters.But i have to cluster 20 face images from database as 3 clusters.How it can be done.What input parameter has to be given for kmeans command.Guide me.

          % Face recognition 
        clear all
        close all
        clc
    % number of images on your training set.
    M=20;
    %Chosen std and mean. 
    %It can be any number that it is close to the std and mean of most of the images.
    um=100;
    ustd=80;
    %read and show images(bmp);
    S=[];   %img matrix
    figure(1);
    for i=1:M
        str=strcat(int2str(i),'.jpg');   %concatenates two strings that form the name of the image
        eval('img=imread(str);');
        subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
        imshow(img)
        if i==3
            title('Training set','fontsize',18)
        end
        drawnow;
           [irow icol d]=size(img); % get the number of rows (N1) and columns (N2)
           temp=reshape(permute(img,[2,1,3]),[irow*icol,d]);     %creates a (N1*N2)x1 matrix
        S=[S temp];         %X is a N1*N2xM matrix after finishing the sequence
                            %this is our S
    end
    %Here we change the mean and std of all images. We normalize all images.
    %This is done to reduce the error due to lighting conditions.
    for i=1:size(S,2)
        temp=double(S(:,i));
        m=mean(temp);
        st=std(temp);
        S(:,i)=(temp-m)*ustd/st+um;
    end
    %show normalized images
    figure(2);
    for i=1:M
        str=strcat(int2str(i),'.jpg');
        img=reshape(S(:,i),icol,irow);
        img=img';
       % eval('imwrite(img,str)');   
        subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
        imshow(img)
        drawnow;
        if i==3
            title('Normalized Training Set','fontsize',18)
        end
    end
    %mean image;
    m=mean(S,2);   %obtains the mean of each row instead of each column
    tmimg=uint8(m);   %converts to unsigned 8-bit integer. Values range from 0 to 255
    img=reshape(tmimg,icol,irow);    %takes the N1*N2x1 vector and creates a N2xN1 matrix
    img=img';       %creates a N1xN2 matrix by transposing the image.
    figure(3);
    imshow(img);
    title('Mean Image','fontsize',18)
    % Change image for manipulation
    dbx=[];   % A matrix
    for i=1:M
        temp=double(S(:,i));
        dbx=[dbx temp];
    end
    %Covariance matrix C=A'A, L=AA'
    A=dbx';
    L=A*A';
    % vv are the eigenvector for L
    % dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx';
    [vv dd]=eig(L);
    % Sort and eliminate those whose eigenvalue is zero
    v=[];
    d=[];
    for i=1:size(vv,2)
        if(dd(i,i)>1e-4)
            v=[v vv(:,i)];
            d=[d dd(i,i)];
        end
     end
     %sort,  will return an ascending sequence
     [B index]=sort(d);
     ind=zeros(size(index));
     dtemp=zeros(size(index));
     vtemp=zeros(size(v));
     len=length(index);
     for i=1:len
        dtemp(i)=B(len+1-i);
        ind(i)=len+1-index(i);
        vtemp(:,ind(i))=v(:,i);
     end
     d=dtemp;
     v=vtemp;
    %Normalization of eigenvectors
     for i=1:size(v,2)       %access each column
       kk=v(:,i);
       temp=sqrt(sum(kk.^2));
       v(:,i)=v(:,i)./temp;
    end
    %Eigenvectors of C matrix
    u=[];
    for i=1:size(v,2)
        temp=sqrt(d(i));
        u=[u (dbx*v(:,i))./temp];
    end
    %Normalization of eigenvectors
    for i=1:size(u,2)
       kk=u(:,i);
       temp=sqrt(sum(kk.^2));
        u(:,i)=u(:,i)./temp;
    end
    % show eigenfaces;
    figure(4);
    for i=1:size(u,2)
        img=reshape(u(:,i),icol,irow);
        img=img';
        img=histeq(img,255);
        subplot(ceil(sqrt(M)),ceil(sqrt(M)),i)
        imshow(img)
        drawnow;
        if i==3
            title('Eigenfaces','fontsize',18)
        end
    end
    % Find the weight of each face in the training set.
    omega = [];
    for h=1:size(dbx,2)
        WW=[];    
        for i=1:size(u,2)
            t = u(:,i)';    
            WeightOfImage = dot(t,dbx(:,h)');
            WW = [WW; WeightOfImage];
        end
        omega = [omega WW];
    end
    % Acquire new image
    % Note: the input image must have a bmp or jpg extension. 
    %       It should have the same size as the ones in your training set. 
    %       It should be placed on your desktop
    ed_min=[];
    for k=0:3
    InputImage = input('Please enter the name of the image and its extension \n','s');
    InputImage = imread(strcat('F:\input image\',InputImage));
    figure(5)
    subplot(1,2,1)
    imshow(InputImage); colormap('gray');title('Input image','fontsize',18)
    %InImage=reshape(double(InputImage),irow*icol,1); 
    InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]);
    temp=InImage;
    me=mean(temp);
    st=std(temp);
    temp=(temp-me)*ustd/st+um;
    NormImage = temp;
    Difference = temp-m;
    p = [];
    aa=size(u,2);
    for i = 1:aa
        pare = dot(NormImage,u(:,i));
        p = [p; pare];
    end
    %ReshapedImage = m + u(:,1:aa)*p;    %m is the mean image, u is the eigenvector
    %ReshapedImage = reshape(ReshapedImage,icol,irow);
    %ReshapedImage = ReshapedImage';
    %show the reconstructed image.
    %subplot(1,2,2)
    %imagesc(ReshapedImage); colormap('gray');
    %title('Reconstructed image','fontsize',18)
    InImWeight = [];
    for i=1:size(u,2)
        t = u(:,i)';
        WeightOfInputImage = dot(t,Difference');
        InImWeight = [InImWeight; WeightOfInputImage];
    end
    %ll = 1:M;
    %figure(68)
    %subplot(1,2,1)
    %stem(ll,InImWeight)
    %title('Weight of Input Face','fontsize',14)
    % Find Euclidean distance
    e=[];
    for i=1:size(omega,2)
        q = omega(:,i);
        DiffWeight = InImWeight-q;
        mag = norm(DiffWeight);
        e = [e mag];
    end
    %kk = 1:size(e,2);
    %subplot(1,2,2)
    %stem(kk,e)
    %title('Eucledian distance of input image','fontsize',14)
    format long
    MaximumValue=max(e)
    MinimumValue=min(e)
    ed_min=[ed_min MinimumValue];
    theta=3.593489431882765e+04;
    %disp(e)
    if(MinimumValue<=theta)
        fprintf('recognized face\n');
    else
        fprintf('unrecognized face\n');
    end
    end
    disp(ed_min);
    [IDX,C] = kmeans(ed_min,3)