I have a Fuzzy-C means function, is there a pro to explain it to me?

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Diboun Mohsen Djawad
回答： 埃博拉酱 2024-10-28，14:57
function [ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum)
% demo
% img = double(imread('brain.tif'));
% clusterNum = 3;
% [ Unow, center, now_obj_fcn ] = FCMforImage( img, clusterNum );
% figure;
% subplot(2,2,1); imshow(img,[]);
% for i=1:clusterNum
% subplot(2,2,i+1);
% imshow(Unow(:,:,i),[]);
% end
if nargin < 2
clusterNum = 8; % number of cluster
end
[row, col] = size(img);
expoNum = 2; % fuzzification parameter
epsilon = 0.001; % stopping condition
mat_iter = 100; % number of maximun iteration
rng('default');
Upre = rand(row, col, clusterNum);
dep_sum = sum(Upre, 3);
dep_sum = repmat(dep_sum, [1,1, clusterNum]);
Upre = Upre./dep_sum;
center = zeros(clusterNum,1);
for i=1:clusterNum
center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
end
pre_obj_fcn = 0;
for i=1:clusterNum
pre_obj_fcn = pre_obj_fcn + sum(sum((Upre(:,:,i) .*img - center(i)).^2));
end
fprintf('Initial objective fcn = %f\n', pre_obj_fcn);
for iter = 1:mat_iter
Unow = zeros(size(Upre));
for i=1:row
for j=1:col
for uII = 1:clusterNum
tmp = 0;
for uJJ = 1:clusterNum
disUp = abs(img(i,j) - center(uII));
disDn = abs(img(i,j) - center(uJJ));
tmp = tmp + (disUp/disDn).^(2/(expoNum-1));
end
Unow(i,j, uII) = 1/(tmp);
end
end
end
now_obj_fcn = 0;
for i=1:clusterNum
now_obj_fcn = now_obj_fcn + sum(sum((Unow(:,:,i) .*img - center(i)).^2));
end
fprintf('Iter = %d, Objective = %f\n', iter, now_obj_fcn);
if max(max(max(abs(Unow-Upre))))<epsilon || abs(now_obj_fcn - pre_obj_fcn)<epsilon
break;
else
Upre = Unow.^expoNum;
for i=1:clusterNum
center(i,1) = sum(sum(Upre(:,:,i).*img))/sum(sum(Upre(:,:,i)));
end
pre_obj_fcn = now_obj_fcn;
end
end

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回答（1 个）

埃博拉酱
埃博拉酱 2024-10-28，14:57
This MATLAB code implements the Fuzzy C-Means (FCM) algorithm for image segmentation. Here's an explanation of what the code does:
  1. The function `FCMforImage` takes an input image and the number of clusters as parameters.
  2. It initializes variables such as the fuzzification parameter, stopping condition, and maximum number of iterations.
  3. The code starts by randomly initializing the membership matrix `Upre` .
  4. It then calculates initial cluster centers based on the membership matrix and image data.
  5. The main loop of the FCM algorithm begins, which iterates until convergence or the maximum number of iterations is reached: It updates the membership matrix `Unow` based on the distances between pixels and cluster centers. It calculates the objective function value for the current iteration. It checks for convergence by comparing the change in membership matrix and objective function value with the stopping condition. If not converged, it updates the cluster centers and continues to the next iteration.
  6. The function returns the final membership matrix `Unow` , cluster centers, and the final objective function value.
  7. There's also a commented-out demo section at the beginning, showing how to use the function and display the results.
This algorithm is used for fuzzy clustering of image data, where each pixel can belong to multiple clusters with different degrees of membership.

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