how to obtain the eigenfaces using eigenvalues and eigen vectors? - matlab

Question

i want to find eigenfaces from eigen values here is the code for reference.
clc;
clear all;
close all;
% I) READ IMAGES
for i = 1:9
img{i} = imread(['C:\Users\shree\Desktop\archana\target\' num2str(i) '.jpg']);
end
%II) CONVERTING TO GRAY SCALE
gray_img=cellfun(#rgb2gray,img,'uniformoutput',false);
%imshow(gray_img{2});
%III) RESIZING GRAY IMAGES
res_img = cellfun(#(x)(imresize(x, [50, 50])), gray_img, 'UniformOutput', false);
%imshow(res_img{2});
%DISPLAYING ALL IMAGE
D=[res_img{1} res_img{2} res_img{3}
res_img{4} res_img{5} res_img{6}
res_img{7} res_img{8} res_img{9}];
figure, imshow(D);
%MEAN IMAGE
mean_img=(res_img{1}+res_img{2}+res_img{3}+res_img{4}+res_img{5}+res_img{6}+res_img{7}+res_img{8}+res_img{9})/9;
figure,imshow(mean_img);
%III)SINGLE VECTOR CONVERSION
vect_img= cellfun(#(x)((x(:))), res_img, 'UniformOutput', false);
%MEAN OF SINGLE VECTOR
mean_vect=(vect_img{1}+vect_img{2}+vect_img{3}+vect_img{4}+vect_img{5}+vect_img{6}+vect_img{7}+vect_img{8}+vect_img{9})/9;
%DEVIATION MATRIX
dev_mat=cellfun(#(x) ((x)-mean_vect),vect_img,'uniformoutput',false);
%imshow(dev_mat{1})
U=[dev_mat{1} dev_mat{2} dev_mat{3} dev_mat{4} dev_mat{5} dev_mat{6} dev_mat{7} dev_mat{8} dev_mat{9} ]
figure ,imshow(U);
%COVARIENCE MATRIX
C=[double(U')*double(U)]/9;
%VARIENCE
v=var(C);
%EIGEN VALUES
lambda = eig(C);
[V,D] = eig(C) ;% eigenvalues (D) & eigenvectors (V),=> A*V = V*D
size(lambda);
% EXTRACT DIONAL OF MATRIX VECTOR
%V = diag(V);
%SORT VARIENCE ACC.DECREASING ORDER
sort(lambda,'descend');
i reached upto the arranging the eiganvalues into non-increasing order plz help me how to procced in order to get the eigenfaces.regards

Answer 1

Instead of loading each file one by one try this
ImageDatabasePath ='C:\Users\shree\Desktop\final data';
ImageFiles = dir(ImageDatabasePath);
Train_Number = 0;
for i = 1:size(ImageFiles,1)
if not(strcmp(ImageFiles(i).name,'.')|strcmp(ImageFiles(i).name,'..')...
|strcmp(ImageFiles(i).name,'Thumbs.db'))
Image_Number = Image_Number + 1;
end
end
Now to make the images into 1D image vectors
T = [ ];
for i = 1 : Image_Number
str = int2str(i);
str = strcat('\',str,'.jpg');
str = strcat(ImageDatabasePath,str);
imt = imread(str);
[irow icol] = size(imt);
temp = reshape(imt,irow*icol,1);
T = [T temp];
end
Calculates mean value
m = mean(T,2);
Train_Number = size(T,2);
Calculates the deviation of each image from the mean image
A = [ ];
for i = 1 : Image_Number
temp = double(T(:,i)) - m;
A = [A temp];
end
Create covariance matrix
L = A'*A;
Calculate eigen values and eigen vector V-eigen vector D-diagonal matrix with eigen values
[V D] = eig(L);
L_eig_vec = [];
for i = 1 : size(V,2)
if( D(i,i)>1 )
L_eig_vec = [L_eig_vec V(:,i)];
end
end
Eigenvectors of covariance matrix C (or so-called "Eigenfaces") can be recovered from L's eiegnvectors.
Eigenfaces = A * L_eig_vec;

Answer 2

Use double(NEW) * double(NEW');
Besides, do not use mean and cov as variable name. They are built-in functions. I guess you want C = cov(double(NEW) * double(NEW')); in the covariance calculation.