【图像分割】基于模糊C均值聚类算法CGFFCM实现彩色图像分割附matlab代码

clc clear all close all % % Load dataset. % Load image Img = imread(’3096.jpg’); % Load the class. B=load(’class3096.mat’); B=B.class3096; class=double(reshape(B,[size(B,1)*size(B,2) 1])); % % Show the image and its Ground truth. % axis off % img = frame2im(getframe(gca)); % imwrite(Img, ’113044.png’ ); Color_Map=[0.960012728938293,0.867750283531604,0.539798806341941; 0.00620528530061783,0.456645636880323,0.807705802687295; 0.16111134052427435,0.0634483119173452,0.321794465524281; 0.394969976441930,0.662682208014249,0.628325568854317; 0.0945269059837998,0.794795142853933,0.473068960772494]; Ground_truth = label2rgb(B,Color_Map); % axis off % img = frame2im(getframe(gca)); % imwrite(Ground_truth, ’class134052.png’ ); % % Feathre Extract step fprintf(’The feature extraction phase has started ... ’) X = FeatureExtractor(Img); [N,d]=size(X); % % Algorithm parameters. % for each image, ICA algorithm run and Group Weight values are reported. % Also, best values for q and landa are listed. % for get best, rsult we use parameters as fallows: % ========================================================================== % Image Weight(G1=V) Weight(G2=V) Weight(G3=V) landa(oo) q(beta_z) % 67079 0.0 1 0.0 0.0001 2 % 101027 0.2 0.6 0.2 0.0001 -2 % 118035 0.4 0.6 0.0 0.0010 -2 % 167062 0.4 0.1 0.5 1.0000 2 % 108073 0.7 0.1 0.2 0.0010 -2 % 113016 0.7 0.2 0.1 0.0100 -8 % 113044 0.3 0.6 0.1 0.1000 4 % 134052 0.5 0.4 0.1 0.1000 6 % 135069 0.1 0.7 0.2 0.0100 -6 % 238011 0.1 0.1 0.8 0.0001 2 % 299091 0.1 0.8 0.1 0.1000 6 % 3096 0.1 0.7 0.2 0.0001 -10 % 80099 0.3 0.6 0.1 0.0100 -2 % ========================================================================== k=size(unique(class),1); % number of clusters. beta_z = -10; % The power value of the feature weight(in paper). p_init = 0; % initial p. p_max = 0.5; % maximum p. p_step = 0.01; % p step. t_max = 100; % maximum number of iterations. beta_memory = 0.3; % amount of memory for the weights updates. Restarts = 1; % number of CGFFCM restarts. fuzzy_degree = 2; % fuzzy membership degree v(1,1:3) = 0.1; % Weight of group 1 v(1,4:6) = 0.7; % Weight of group 2 v(1,7:8) = 0.2; % Weight of group 3 G = [1 1 1 2 2 2 3 3]; % Feature Groups (three group 1, 2 and 3) oo=0.0001; % interval (0,1] landa=oo./var(X); % the inverse variance of the m-th feature TF = find(isinf(landa)==1); if ~isempty(TF) for i=1:size(TF,2) landa(1,TF(i))=nan; end aa=max(landa); for i=1:size(TF,2) landa(1,TF(i))=aa+1; end end % % Cluster the instances using the CGFFCM procedure. best_clustering=zeros(1,N); for repeat=1:Restarts fprintf(’======================================================== ’) fprintf(’CGFFCM: Restart %d ’,repeat); %Randomly initialize the cluster centers. rand(’state’,repeat) tmp=randperm(N); M=X(tmp(1:k),:); %Execute CGFFCM. %Get the cluster assignments, the cluster centers and the cluster variances. [Cluster_elem,M,EW_history,W,z]=CGFFCM(X,M,k,p_init,p_max,p_step,t_max,beta_memory,N,fuzzy_degree,d,beta_z,landa,v,G); [~,Cluster]=max(Cluster_elem,[],1); %Meaures EVAL = Evaluate(class,Cluster’); accurcy_ave(repeat)=EVAL(1); fm_ave(repeat)=EVAL(2); nmi_ave(repeat)=EVAL(3); if best_clustering ~= 0 if accurcy_ave(repeat) > accurcy_ave(repeat-1) best_clustering = Cluster; end else best_clustering = Cluster; end fprintf(’End of Restart %d ’,repeat); fprintf(’======================================================== ’) end % % Results % Show the best segmented image cmap = reshape(best_clustering’, [size(double(Img),1) size(double(Img),2)]); clusteredImage = label2rgb(cmap,Color_Map); figure(1),subplot(131),imshow(Img),title(’Original Image’); ,subplot(132),imshow(Ground_truth),title(’Ground truth Image’); ,subplot(133),imshow(clusteredImage),title(’The segmented Image’); % axis off % img = frame2im(getframe(gca)); % imwrite(clusteredImage, ’cluster134052.png’ ); fprintf(’Average accurcy score over %d restarts: %f. ’,Restarts,mean(accurcy_ave)); fprintf(’Average F-Measure score over %d restarts: %f. ’,Restarts,mean(fm_ave)); fprintf(’Average NMI score over %d restarts: %f. ’,Restarts,mean(nmi_ave));