KNN segmentation for MATLAB: https://www.youtube.com/watch?v=3hEvcyCJNRc

knn_image_segmentation_matlab.m

clear all, close all, clc

%% read the image
x = imread('balls.jpg');

x = imresize(x, 0.3);

figure()
imshow(x)

[r,c,s] = size(x);

%% Initialize storage for each sample region
classes = {'orange','purple','green','background'};
nClasses = length(classes);
sample_regions = false([r c nClasses]);

%% Select each sample region
f = figure;
for count = 1:nClasses
    set(f, 'name', ['Select sample region for ' classes{count}]);
    sample_regions(:,:,count) = roipoly(x);
end

close(f);

%% Display sample regions
for count = 1:nClasses
    figure
    imshow(sample_regions(:,:,count))
    title(['sample region for ' classes{count}]);
end

%% Convert to L*a*b
cform = makecform('srgb2lab');
lab_x = applycform(x,cform);

figure()
imshow(lab_x)

%% Calculate the mean 'a*' and 'b*' value for each ROI area
a = lab_x(:,:,2);
b = lab_x(:,:,3);
color_markers = repmat(0, [nClasses, 2]);

for count = 1:nClasses
    color_markers(count,1) = mean(a(sample_regions(:,:,count)));
    color_markers(count,2) = mean(b(sample_regions(:,:,count)));
end

%% Classify each pixel using the nearest neighbor rule

color_labels = 0:nClasses-1;
a = double(a);
b = double(b);
distance = repmat(0, [size(a), nClasses]);

%% Perform classification

for count = 1:nClasses
    distance(:,:,count) = ((a - color_markers(count,1)).^2 + ...
                            (b - color_markers(count,2)).^2);
end

[value, label] = min(distance,[],3); % as label it return the index,, in our case it is count
label = color_labels(label); % Makes it from 0 to 3 instead of from 1 to 4

%% clear value distance

% colors = [255 0 0; 0 255 0; 0 0 255; 255 255 0; 255 0 255; 0 255 255]; % R G B R-G R-B G-B
colors = [255 0 0; 0 255 0; 0 0 255; 255 255 0]; % R G B R-G(Yellow) Same number as classes in the picture

y = zeros(size(x));
l = double(label) + 1;

for m = 1:r
    for n = 1:c
        y(m,n,:) = colors(l(m,n),:);
    end
end

figure()
imshow(y)
colorbar


%% Scatter plot for the nearest neighbour classification
purple = [119/255 73/255 152/255];
plot_labels = {'k', 'r', 'g', purple};

figure()
for count = 1:nClasses
    plot(a(label == count-1), b(label == count-1),'.','MarkerEdgeColor',...
        plot_labels{count}, 'MarkerFaceColor', plot_labels{count});
    hold on
end

title('Scatterplot of the segmented pixels in ''a*b*'' space');
xlabel('''a*'' values');
ylabel('''b*'' values');