function [k, class, img_vect] = kmeans(img, k);

img = double(img);

img = imresize(img, [256,256]);

img_vect = img(:);

centroid = zeros(k,1);

class = zeros(length(img_vect), k);

%initialize centroid

maximum = max(img_vect);

for cent = 1:k

    centroid(cent,1)= cent * maximum / k;

end

iter = 0;

while(iter<10)

    class(1:length(img_vect),1:k) = 0;

    % classifying pixels

    for i = 1: length(img_vect)

        [val, ind]=min(abs(img_vect(i) - centroid((1:k),1)));

        class(i,ind)= img_vect(i);

    end

    % updating centroid

    for cent = 1:k

        centroid(cent, 1)= sum(class(:,cent))/length(find(class(:,cent)));

    end

    iter = iter +1; 

end

%figure;imshow(img,[]),title('original');

% for clust = 1:k

%     cluster = reshape(class(1:length(img_vect),clust:clust), [256,256] );

%     figure; imshow(cluster,[]),title('cluster');

% end