MATLAB: Ideas to improve per-pixel classification

Image Processing Toolboxper-pixelsvm

Hello,

I'm working on a pixel classification of an image. I have 4 classes and they are using RGB coefficients. I have one problem using SVM classification, there is 2 of the classes wich are similar. In the confusion matrix, I have an error of 40% between that 2 classes. They have pretty high similar colors. Does anyone have an idea or advice for improving the accuracy of the classification? For example adding more predictive or adding more contrast between these 2 problematic classes

Thank you

Best Answer

What is the basis for your classification? Color? Are you comparing your image to 4 known reference colors? Then try using Delta E or LDA.

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MATLAB: Vectorize a per-pixel classification

The key is recognizing that:

diag(R*A*R') == sum(R*A.*R,2);

Try the following code. It does exactly what your code does, using only one loop, and runs 100 times faster.

clc
I = imread('peppers.png'); I = double(I)/255;
[height,width,~] = size(I);
image = I;
noOfClasses = 5;
pDet = rand(noOfClasses,1);
pInv = rand(3,3,noOfClasses);
pMean = rand(noOfClasses,3);
% ----------------------The original way----------------------
disp('First do it the original way...')
tic
post(height,width,noOfClasses) = 0;
for count=1:height
      for count2=1:width
          %pixel vector x to be classified
          x=[image(count,count2,1);image(count,count2,2);image(count,count2,3)];
             %for each class
              for count3=1:noOfClasses
                detZ = pDet(count3);
                invZ = pInv(:,:,count3);
                y=(pMean(count3,:)');
                %calculate the likelihood
                post(count,count2,count3)= 1/sqrt((2*pi)^2*detZ) * exp(-(x(:,1)-y)'*invZ*(x(:,1)-y)/2);
              end
      end
end
originalway = toc
% ----------------------The fast way----------------------
disp('Now do it the fast way...')
RGB = reshape(I,[],3);
%[colorlist, ~, ind] = unique(RGB,'rows');
colorlist = RGB;
C = 1./sqrt((2*pi)^2*pDet);
ncol = size(colorlist,1);
tic
post2(height,width,noOfClasses) = 0;
      for count3=1:noOfClasses
          invZ = pInv(:,:,count3);
          y=(pMean(count3,:)');
          colorlistY = bsxfun(@minus,colorlist,y');
          post2(:,:,count3) = reshape(C(count3) * exp(-sum(colorlistY*invZ.*colorlistY,2)/2) , height, width);
      end
  fasterway = toc
  ARETHEYTHESAME = isequal(post,post2)

Best Answer

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MATLAB: How to train fitcecoc by using COST SENSITIVE SVM

MATLAB: Vectorize a per-pixel classification

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