【18】【图像处理】粒子群算法结合模糊聚类分割算法实现图像的分割
作者：互联网
%pso-fcm。算法思路借鉴网上的某一帖子。tic;close all;clear;clc;pic=imread('2.png'); [a,b,k]=size(pic);t=a*b;data=reshape(double(pic),t,k);Maxiter=4;%设定最大迭代次数n=100;c1=0.4;c2=0.4;%设定个体经验系数和群体经验系数w=0.3;%设定惯性系数vmax=1.5;%设定最大速度cmax=4;%设定最大粒子（聚类中心）数目e=1e+4;%设定阈值ref=2;%设定fcm的系数result=zeros(cmax-1,1);u=cell(cmax,n);vit=cell(cmax,n);particle=cell(cmax,n);dist=cell(cmax,n);obj=zeros(cmax,n);pbest=cell(cmax,1);pbest_pos=cell(cmax,n);gbest=zeros(cmax,1);gbest_index=zeros(cmax,1);gbest_pos=cell(cmax,1);for c=2:cmax u_new=zeros(t,c); for i=1:n x=randperm(t); for j=1:c particle{c,i}(j,:)=data(x(j),:);%随机取c个样本作为初始粒子 end vit{c,i}=rand(c,3).*vmax; u{c,i}=zeros(t,c); dist{c,i}=distfcm(particle{c,i},data); dist{c,i}=dist{c,i}+0.01; tmp=dist{c,i}.^(-2/(ref-1)); u{c,i}=tmp./(ones(c,1)*sum(tmp)); %更新隶属度矩阵（初始） [u_new,particle{c,i},obj(c,i)]=stepfcm(data,u{c,i},c,ref); u{c,i}=u_new; %更新粒子和隶属度矩阵（fcm） end iter=1; fit(c)=1e+9; while(iter<=Maxiter&fit(c)>e) if(iter==1) for i=1:n pbest_pos{c,i}=particle{c,i}; end pbest{c}=obj(c,1:n); gbest_index(c)=find(pbest{c}==min(pbest{c})); gbest_pos{c}=pbest_pos{c,gbest_index(c)}; gbest(c)=pbest{c}(gbest_index(c)); else replace=0; replace=find(obj(c,:)<pbest{c}); if(size(replace)>0) for i=1:size(replace) pbest_pos{c,replace(i)}=particle{c,replace(i)}; pbest{c}(replace(i))=obj(c,replace(i)); end end mins=find(pbest{c}==min(pbest{c})); gbest_index(c)=mins(1); gbest_pos{c}=pbest_pos{c,gbest_index(c)}; gbest(c)=pbest{c}(gbest_index(c)); end for i=1:n vit{c,i}=vit{c,i}.*w+(pbest_pos{c,i}-particle{c,i}).*c1+(-particle{c,i}+gbest_pos{c}).*c2; for j=1:c if (norm(vit{c,i}(j,:))>=vmax) vit{c,i}(j,:)=(vit{c,i}(j,:).*vmax)./norm(vit{c,i}(j,:)); end end end%超过最大速度则限制为最大速度保持方向不变 for i=1:n particle{c,i}=particle{c,i}+vit{c,i}; end for i=1:n [u{c,i},particle{c,i},obj(c,i)]=stepfcm(data,u{c,i},c,ref); end fit(c)=min(obj(c,:)); iter=iter+1 end result(c-1)=gbest(c)endbestc=find(result==min(result))+1bestresult=min(result)bestparticle=gbest_pos{bestc};bestu=u{bestc,gbest_index(bestc)};cluster = cell(1,bestc);cluster_pic = cell(1,bestc);pic = cell(1,bestc);result_pic=cell(1,bestc);for i = 1:bestc cluster{i} = find( u{bestc,gbest_index(bestc)}(i,:)==max(u{bestc,gbest_index(bestc)})); cluster{i} = cluster{i}'; cluster_pic{i} = data(1:t,1:3); temp = data(1:t,1:3); count=0; for j = 1:bestc if bestparticle(j,1)>bestparticle(i,1) count=count+1; end end endtoc;
注：完整代码或者代写添加QQ1575304183
标签：分割,end,particle,gbest,18,pbest,bestc,算法,cmax 来源： https://www.cnblogs.com/homeofmatlab/p/14206347.html