how to implement code of cluster based segmentation on image?
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I have a kmean clutering for segmenting any gray or color image,i want to know what changes i have to do to implement it in any image .please help me to sort out?
function [lb,center] = adaptcluster_kmeans(im)
% This code is written to implement kmeans clustering for segmenting any % Gray or Color image. There is no requirement to mention the number of cluster for % clustering. % IM - is input image to be clustered. % LB - is labeled image (Clustered Image). % CENTER - is array of cluster centers. % Execution of this code is very fast. % It generates consistent output for same image.
% Written by Ankit Dixit. % January-2014.
if size(im,3)>1 [lb,center] = ColorClustering(im); % Check Image is Gray or not. else [lb,center] = GrayClustering(im); end
function [lb,center] = GrayClustering(gray) gray = double(gray); array = gray(:); % Copy value into an array. % distth = 25; i = 0;j=0; % Intialize iteration Counters. tic while(true) seed = mean(array); % Initialize seed Point. i = i+1; %Increment Counter for each iteration. while(true) j = j+1; % Initialize Counter for each iteration. dist = (sqrt((array-seed).^2)); % Find distance between Seed and Gray Value. distth = (sqrt(sum((array-seed).^2)/numel(array)));% Find bandwidth for Cluster Center. % distth = max(dist(:))/5; qualified = dist<distth;% Check values are in selected Bandwidth or not. newseed = mean(array(qualified));% Update mean.
if isnan(newseed) % Check mean is not a NaN value.
break;
end
if seed == newseed || j>10 % Condition for convergence and maximum iteration.
j=0;
array(qualified) = [];% Remove values which have assigned to a cluster.
center(i) = newseed; % Store center of cluster.
break;
end
seed = newseed;% Update seed.
end
if isempty(array) || i>10 % Check maximum number of clusters.
i = 0; % Reset Counter.
break;
end
end toc
center = sort(center); % Sort Centers. newcenter = diff(center);% Find out Difference between two consecutive Centers. intercluster = (max(gray(:)/10));% Findout Minimum distance between two cluster Centers. center(newcenter<=intercluster)=[];% Discard Cluster centers less than distance.
% Make a clustered image using these centers.
vector = repmat(gray(:),[1,numel(center)]); % Replicate vector for parallel operation. centers = repmat(center,[numel(gray),1]);
distance = ((vector-centers).^2);% Find distance between center and pixel value. [~,lb] = min(distance,[],2);% Choose cluster index of minimum distance. lb = reshape(lb,size(gray));% Reshape the labelled index vector.
function [lb,center] = ColorClustering(im)
im = double(im); red = im(:,:,1); green = im(:,:,2); blue = im(:,:,3);
array = [red(:),green(:),blue(:)]; % distth = 25; i = 0;j=0; tic while(true)
seed(1) = mean(array(:,1));
seed(2) = mean(array(:,2));
seed(3) = mean(array(:,3));
i = i+1;
while(true)
j = j+1;
seedvec = repmat(seed,[size(array,1),1]);
dist = sum((sqrt((array-seedvec).^2)),2);
distth = 0.25*max(dist);
qualified = dist<distth;
newred = array(:,1);
newgreen = array(:,2);
newblue = array(:,3);
newseed(1) = mean(newred(qualified));
newseed(2) = mean(newgreen(qualified));
newseed(3) = mean(newblue(qualified));
if isnan(newseed)
break;
end
if (seed == newseed) | j>10
j=0;
array(qualified,:) = [];
center(i,:) = newseed;
% center(2,i) = nnz(qualified);
break;
end
seed = newseed;
end
if isempty(array) || i>10
i = 0;
break;
end
end toc centers = sqrt(sum((center.^2),2)); [centers,idx]= sort(centers);
while(true) newcenter = diff(centers); intercluster =25; %(max(gray(:)/10)); a = (newcenter<=intercluster); % center(a,:)=[]; % centers = sqrt(sum((center.^2),2)); centers(a,:) = []; idx(a,:)=[]; % center(a,:)=0; if nnz(a)==0 break; end
end center1 = center; center =center1(idx,:); % [~,idxsort] = sort(centers) ; vecred = repmat(red(:),[1,size(center,1)]); vecgreen = repmat(green(:),[1,size(center,1)]); vecblue = repmat(blue(:),[1,size(center,1)]);
distred = (vecred - repmat(center(:,1)',[numel(red),1])).^2; distgreen = (vecgreen - repmat(center(:,2)',[numel(red),1])).^2; distblue = (vecblue - repmat(center(:,3)',[numel(red),1])).^2;
distance = sqrt(distred+distgreen+distblue); [~,label_vector] = min(distance,[],2); lb = reshape(label_vector,size(red)); %
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