Use Eigenfaces
as the training set, compose a label
vector with 1 or -1s (if the ith column of Eigenfaces
refers to 1, then the ith element in label
is 1, otherwise it is -1) . And use Eigenfaces
and label
in svmtrain
function.
Why is the accuracy coming as 0% ? MATLAB LIBSVM
-
05-10-2022 - |
سؤال
I extracted PCA features using:
function [mn,A1,A2,Eigenfaces] = pca(T,f1,nf1)
m=mean(T,2), %T is the whole training set
train=size(T,2);
A=[];
for i=1:train
temp=double(T(:,i))-m;
A=[A temp];
end
train=size(f1,2); %f1 - Face 1 images from training set 'T'
A=[];
for i=1:train
temp=double(f1(:,i))-m;
A1=[A1 temp];
end
train=size(nf1,2); %nf1 - Images other than face 1 from training set 'T'
A=[];
for i=1:train
temp=double(nf1(:,i))-m;
A2=[A2 temp];
end
L=A'*A;
[V D]=eig(L);
for i=1:size(V,2)
if(D(i,i)>1)
L_eig=[L_eig V(:,1)];
end
end
Eigenfaces=A*L_eig;
end
Then i projected only the face 1(class +1) from training data as such :
Function 1
for i=1:15 %number of images of face 1 in training set
temp=Eigenfaces'*A1(:,i);
proj_img1=[proj_img1 temp];
end
Then i projected rest of the faces(class -1) from training data as such :
Function 2
for i=1:221 %number of images of faces other than face 1 in training set
temp=Eigenfaces'*A2(:,i);
proj_img2=[proj_img2 temp];
end
Function 3 Then the input image vector was obtained using:
diff=double(inputimg)-mn; %mn is the mean of training data
testfeaturevector=Eigenfaces'*diff;
I wrote the results of Function 1 and 2 in a CSV file with labels +1 and -1 respectively. I then used LIBSVM to obtain the accuracy when giving the true label, it returned 0% and when i tried to predict the label it was -1 instead of +1.
And the accuracy coming as 0% ?
Basically my model is not trained properly and i am failing to see the error.
Any suggestions will be greatly appreciated.
المحلول
نصائح أخرى
@lennon310:
for i=1:length(Eigenfaces)
temp=Eigenfaces'*A(:,i);
proj_imgs=[proj_imgs temp];
end
@lennon310:
diff=double(inputimg)-mn; %mn is the mean of training data
testfeaturevector=Eigenfaces'*diff;
Frankly, your code is a mess.
One questionable part:
data = reshape(data, M*N,1);
Doesn't this make data
a matrix with just 1 column? This does not make sense.
Look at this tutorial on eigenfaces. There is code and examples within it to show you what to do. See the related webpage here for more details. The Matlab/Octave code can be found here.
@lennon310:
temp=double(testimg)-m; %where 'm' is the mean of the training images
L=temp'*temp;
[V D]=eig(L);
for i=1:size(V,2)
if(D(i,i)>1)
L_eig=[L_eig V(:,1)];
end
end
Eigenfaces=temp*L_eig;