please in details can anyone explain this code of K means Segmentation?

1 visualización (últimos 30 días)
clear all
close all
[filename,pathname] = uigetfile({'*.*';'*.bmp';'*.tif';'*.gif';'*.png'},'Pick an Image File');
I = im2double(imread([pathname,filename]));
[rows, columns, numberOfColorChannels] = size(I);
F = reshape(I, rows*columns, numberOfColorChannels);
******* From here please explain
K = 3; % Cluster Numbers
CENTS = F( ceil(rand(K,1)*size(F,1)) ,:); % Cluster Centers
DAL = zeros(size(F,1),K+2); % Distances and Labels
KMI = 50; % K-means Iteration
for n = 1:KMI
for i = 1:size(F,1)
for j = 1:K
DAL(i,j) = norm(F(i,:) - CENTS(j,:));
end
[Distance, CN] = min(DAL(i,1:K)); % 1:K are Distance from Cluster Centers 1:K
DAL(i,K+1) = CN; % K+1 is Cluster Label
DAL(i,K+2) = Distance; % K+2 is Minimum Distance
end
for i = 1:K
A = (DAL(:,K+1) == i); % Cluster K Points
CENTS(i,:) = mean(F(A,:)); % New Cluster Centers
if sum(isnan(CENTS(:))) ~= 0 % If CENTS(i,:) Is Nan Then Replace It With Random Point
NC = find(isnan(CENTS(:,1)) == 1); % Find Nan Centers
for Ind = 1:size(NC,1)
CENTS(NC(Ind),:) = F(randi(size(F,1)),:);
end
end
end
end
X = zeros(size(F));
for i = 1:K
end idx = find(DAL(:,K+1) == i);
X(idx,:) = repmat(CENTS(i,:),size(idx,1),1);
end
  2 comentarios
Image Analyst
Image Analyst el 28 de Dic. de 2017
Editada: Image Analyst el 28 de Dic. de 2017
Please read this and fix your formatting.
Next, read this link and then attach your image file.\
In the meantime, is the author answering your questions?
Touhidul islam
Touhidul islam el 28 de Dic. de 2017
sir , i have done all the necessary things you had asked me to do.

Iniciar sesión para comentar.

Respuestas (1)

Bernhard Suhm
Bernhard Suhm el 5 de En. de 2018
You need to provide some more context, what are you trying to accomplish? Right now, this feels like a coding puzzle. By my interpretation, this code clusters all the pixes of the input image into 3 clusters (running k-means KMI times), and then replaces each pixel by its cluster.

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