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基于花授粉的模糊聚类的图像分割（MATLAB）

花匠小妙招
2024-11-01 22:13

基于花授粉的模糊聚类

（自学整理代码，如有错误之处，还望各位大神留言指导）

function [best,fmin,U,N_iter]=C_FPA(para, data, k, expo, min_impro) % min_impro:隶属度最小变化量，迭代终止条件 % expo: 隶属度矩阵U的加权指数 n=para(1); % Population size, typically 10 to 25 p=para(2); % 转换概率 % Iteration parameters N_iter=1000; % Total number of iterations % Dimension of the search variables d=2; Lb=min(data)*ones(1,d); Ub=max(data)*ones(1,d); % Initialize the population/solutions for i=1:n % Sol(i,:)=Lb+(Ub-Lb).*rand(1,d); Fitness1 = []; U = rand(k,size(data,1)); col_sum = sum(U); U = U./col_sum(ones(k,1),:); mf = U.^expo; center(:,i) = mf*data./((ones(size(data,2),1)*sum(mf'))'); Fitness1 = distfcm(center,data); Fitness(i) = sum(sum((Fitness1.^2)*mf')); end % Find the current best [fmin,I]=min(Fitness); best=center(:,I); S=center; % Start the iterations -- Flower Algorithm for t=1:N_iter % Loop over all bats/solutions for i=1:n if rand>p %局部授粉 L=Levy(d); dS=L'.*(center(:,i)-best); S(:,i)=center(:,i)+dS; S(:,i)=simplebounds(S(:,i),Lb,Ub); else %全局授粉 epsilon=rand; JK=randperm(n); S(:,i)=S(:,i)+epsilon*(center(:,JK(1))-center(:,JK(2))); S(:,i)=simplebounds(S(:,i),Lb,Ub); end % 更新新一代的花朵与适应度（函数变量和函数值） Fitness1 = []; Fitness1 = distfcm(S(:,i),data); Fnew(i) = sum(sum((Fitness1.^2)*mf')); if (Fnew(i)<=Fitness(i)) center(:,i)=S(:,i); Fitness(i)=Fnew(i); end if Fnew(i)<=fmin best=S(:,i) ; fmin=Fnew(i) ; end end if round(t/10)==t/10 best fmin end %终止条件判别 if i>1 if abs(Fnew(i)-Fnew(i-1))<min_impro break; end end tmp = Fitness1.^(-2/(expo-1)); U = tmp./sum(tmp); %隶属度矩阵 end disp(['Total number of evaluations: ',num2str(N_iter*n)]); disp(['fmin=', num2str(fmin)]); % Application of simple constraints function s=simplebounds(s,Lb,Ub) % Apply the lower bound ns_tmp=s; I=ns_tmp<Lb; ns_tmp(I)=Lb(I); % Apply the upper bounds J=ns_tmp>Ub; ns_tmp(J)=Ub(J); % Update this new move s=ns_tmp; % 飞行参数 function L=Levy(d) beta=3/2; sigma=(gamma(1+beta)*sin(pi*beta/2)/(gamma((1+beta)/2)*beta*2^((beta-1)/2)))^(1/beta); u=randn(1,d)*sigma; v=randn(1,d); step=u./abs(v).^(1/beta); L=0.01*step; %%子函数计算距离 function out=distfcm(center,data) out=zeros(size(center,1),size(data,1)); for k=1:size(center,1) out(k,:)=sqrt(sum(((data-ones(size(data,1),1)*center(k,:)).^2)',1)); end

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调用

close all clc; clear; I=imread('3096.jpg'); % figure; imshow(I,[]);title('yuan图像'); if size(I,3) == 3 I=rgb2gray(I); else end % figure;imshow(I);title('图像'); %imwrite(I,'1.tiff','tiff','Resolution',300);%保存为tif g = double(I); [p q] = size(g); data = g(:); data_n = size(data,1); in_n = size(data,2); k = 2;%类别数 expo = 2; min_impro = 1e-5; para = [25 0.8]; [best,fmin,U,N_iter] = C_FPA(para, data, k, expo, min_impro); maxU = max(U); for j=1:k index = find(U(j, :) == maxU); fcm_label(index) = j; end fcm_result=reshape(fcm_label,[p q]); figure, imshow(fcm_result,[]); title('fpa_fcm分割结果');

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