colorAutoCorrelogram.m

function colorAutoCorrelogram = colorAutoCorrelogram(image)
% input: image in uint8 form, from wich to extract the color auto correlogram
% output: 1x64 feature vector containing the color auto correlogram

% quantize image into 64 colors = 4x4x4, in RGB space
[img_no_dither, map] = rgb2ind(image, 64, 'nodither');
% figure, imshow(img_no_dither, map);

rgb = ind2rgb(img_no_dither, map); % rgb = double(rgb)
% imshow(rgb);
% rgb = cat(3, r, g, b);

% clear workspace
clear('img_no_dither');

% 4 predefined distances between
% neighbor pixel intensities
% according to "Image Indexing Using Color Correlograms" paper
distances = [1 3 5 7];

colorAutoCorrelogram = correlogram(rgb, map, distances);
% For some images like documents, its colorAutoCorrelogram contains less than 64.
% It is a compromise.
colorAutoCorrelogramFix = zeros(1,64);
for i = 1, size(colorAutoCorrelogram, 2)
    colorAutoCorrelogramFix(i) = colorAutoCorrelogram(i);
end
colorAutoCorrelogram = reshape(colorAutoCorrelogramFix, [4 4 4]);

% consturct final correlogram using distances
colorAutoCorrelogram(:, :, 1) = colorAutoCorrelogram(:, :, 1)*distances(1);
colorAutoCorrelogram(:, :, 2) = colorAutoCorrelogram(:, :, 2)*distances(2);
colorAutoCorrelogram(:, :, 3) = colorAutoCorrelogram(:, :, 3)*distances(3);
colorAutoCorrelogram(:, :, 4) = colorAutoCorrelogram(:, :, 4)*distances(4);

% reform it to vector format
colorAutoCorrelogram = reshape(colorAutoCorrelogram, 1, 64);

end

% check if point is a valid pixel
function valid = is_valid(X, Y, point)
    if point(1) < 0 || point(1) >= X
        valid = 0;
    end
    if point(2) < 0 || point(2) >= Y
        valid = 0;
    end
    valid = 1;
end

% find pixel neighbors
function Cn = get_neighbors(X, Y, x, y, dist)
    cn1 = [x+dist, y+dist];
    cn2 = [x+dist, y];
    cn3 = [x+dist, y-dist];
    cn4 = [x, y-dist];
    cn5 = [x-dist, y-dist];
    cn6 = [x-dist, y];
    cn7 = [x-dist, y+dist];
    cn8 = [x, y+dist];
 
    points = {cn1, cn2, cn3, cn4, cn5, cn6, cn7, cn8};
    Cn = cell(1, length(points));
 
    for ii = 1:length(points)
        valid = is_valid(X, Y, points{1, ii});
        if (valid)
        Cn{1, ii} = points{1, ii};
        end
    end
    
end
 
% get correlogram
function colors_percent = correlogram(photo, Cm, K)
    [X, Y, ttt] = size(photo);
    colors_percent = [];
 
%     for k = 1:length(K) % loop over distances
    for k = 1:K % loop over distances
        countColor = 0;
 
        color = zeros(1, length(Cm));
 
        for x = 2:floor(X/10):X % loop over image width
           for y = 2:floor(Y/10):Y % loop over image height
               Ci = photo(x, y);
%                Cn = get_neighbors(X, Y, x, y, K(k));
               Cn = get_neighbors(X, Y, x, y, k);
 
               for jj = 1:length(Cn) % loop over neighbor pixels
                   Cj = photo( Cn{1, jj}(1), Cn{1, jj}(2) );
 
                   for m = 1:length(Cm) % loop over map colors
                       if isequal(Cm(m), Ci) && isequal(Cm(m), Cj)
                           countColor = countColor + 1;
                           color(m) = color(m) + 1;
                       end
                   end
               end
           end
        end
 
        for ii = 1:length(color)
            color(ii) = double( color(ii) / countColor );
        end
 
        colors_percent = color;
    end
end