MATLAB: How to extract the highest 300 coefficient from each level of decomposition in shearlet transform( from level 1 to 5).

image analysisimage processingMATLAB

I have extracted some statistical features (mean, standard deviation, constranst.. etc), but Now I want to test using highest 300 coefficients from each level of decompostion… please I need your suggestion. Thank you
      if nargin < 1
      img = double(imread('Img205.png','png'));
  end
  [row col] = size(img);
  ndir = [2 2 1 1 0];
for j = 1:5
    for k = -2^(ndir(j)):2^(ndir(j))
        coeff_h{j}(:,:,k+(2^(ndir(j))+1)) = obtain_shear_coeff(img,[3 3 4 4 5],ndir(j),'hor',j,k);
        coeff_v{j}(:,:,k+(2^(ndir(j))+1)) = obtain_shear_coeff(img,[3 3 4 4 5],ndir(j),'ver',j,k);
    end
end
% Display original image
colormap gray;
subplot(1,1,1), imagesc( img, [0 255] ) ; 
title( sprintf('Original Image' )) ;
% Display shearlet coeff for horizontal cone
figure; clf;
colormap gray;
tmp1 = []; tmp2 = []; tmp3 = []; tmp4 = []; tmp5 = [];
for k = 1:9
    if k < 9 
        tmp1 = [ tmp1; coeff_h{1}(:,:,k); 255*ones(5,col/2) ];
    else
        tmp1 = [ tmp1; coeff_h{1}(:,:,k)];
    end
end
for k = 1:9
    if k < 9 
        tmp2 = [ tmp2; coeff_h{2}(:,:,k); 255*ones(5,col/4) ];
    else
        tmp2 = [ tmp2; coeff_h{2}(:,:,k)];
    end
end
for k = 1:5
    if k < 5
        tmp3 = [ tmp3; coeff_h{3}(:,:,k); 255*ones(5,col/8) ];
    else
        tmp3 = [ tmp3; coeff_h{3}(:,:,k)];
    end
end
for k = 1:5
    if k < 5 
        tmp4 = [ tmp4; coeff_h{4}(:,:,k); 255*ones(5,col/16) ];
    else
        tmp4 = [ tmp4; coeff_h{4}(:,:,k)];
    end
end
for k = 1:3
    if k < 3
        tmp5 = [ tmp5; coeff_h{5}(:,:,k); 255*ones(5,col/32) ];
    else
        tmp5 = [ tmp5; coeff_h{5}(:,:,k)];
    end
end
subplot(1,5,1), imagesc( tmp1 ) ; 
subplot(1,5,2), imagesc( tmp2 ) ; 
subplot(1,5,3), imagesc( tmp3 ) ; 
title( sprintf('Shearlet Coefficients for horizontal cone' )) ;
subplot(1,5,4), imagesc( tmp4 ) ; 
subplot(1,5,5), imagesc( tmp5 ) ; 
% Display shearlet coeff for vertical cone 
figure; clf;
colormap gray;
tmp1 = []; tmp2 = []; tmp3 = []; tmp4 = []; tmp5 = [];
for k = 1:9
    if k < 9
        tmp1 = [ tmp1 coeff_v{1}(:,:,k) 255*ones(row/2,5) ];
    else
        tmp1 = [ tmp1 coeff_v{1}(:,:,k)];
    end
end
for k = 1:9
    if k < 9
        tmp2 = [ tmp2 coeff_v{2}(:,:,k) 255*ones(row/4,5) ];
    else
        tmp2 = [ tmp2 coeff_v{2}(:,:,k)];
    end
end
for k = 1:5
    if k < 5
        tmp3 = [ tmp3 coeff_v{3}(:,:,k) 255*ones(row/8,5) ];
    else
        tmp3 = [ tmp3 coeff_v{3}(:,:,k)];
    end
end
for k = 1:5
    if k < 5
        tmp4 = [ tmp4 coeff_v{4}(:,:,k) 255*ones(row/16,5) ];
    else
        tmp4 = [ tmp4 coeff_v{4}(:,:,k)];
    end
end
for k = 1:3
    if k < 3
        tmp5 = [ tmp5 coeff_v{5}(:,:,k) 255*ones(row/32,5) ];
    else
        tmp5 = [ tmp5 coeff_v{5}(:,:,k)];
    end
end
subplot(5,1,1), imagesc( tmp1 ) ; 
title( sprintf('Shearlet Coefficients for vertical cone' )) ;
subplot(5,1,2), imagesc( tmp2 ) ; 
subplot(5,1,3), imagesc( tmp3 ) ; 
subplot(5,1,4), imagesc( tmp4 ) ; 
subplot(5,1,5), imagesc( tmp5 ) ; 
%
% Copyright (c) 2010. Wang-Q Lim

Best Answer

[sorted_data, sort_index] = sort(DataYouWantToGetTop300ValuesOf);
top300values = sorted_data(end-299:end);
index_of_top300values = sort_index(end-299:end);
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