swtu.m

改进的模糊聚类算法

%输入的为三维RGB图
function [U,b,a,the,G]=swtu(g,n0,v,c)
g=double(g)/255;
x1=g(:,:,1);
X1=x1(:);
x2=g(:,:,2);
X2=x2(:);
x3=g(:,:,3);
X3=x3(:);
m=2;
the=0.1;
thresh=0.001;
[h,w]=size(x1);
n=h*w;
U=rand(n,c);
Y=sum(U,2);
for j=1:c
    U(:,j)=U(:,j)./Y;
end
b=0;
for q=1:60
%while the>thresh
    b=b+1;
    U1=U;
    for j=1:c
        Y=U(:,j).^m; 
        fz1=sum(Y.*X1);
        V1(j)=fz1./sum(Y);        
        fz2=sum(Y.*X2);
        V2(j)=fz2./sum(Y);
        fz3=sum(Y.*X3);
        V3(j)=fz3./sum(Y);
    end 
%计算特征不相似性度量
    for j=1:c
        d1(:,j)=(abs(X1-V1(j))+abs(X2-V2(j))+abs(X3-V3(j)))/3;
    end
    %计算空间不相似性度量
    UU=rylzcx(U,x1);%c程序
    s1=sum(UU,2);
        for j=1:c
            d2(:,j)=1-(UU(:,j)./s1);
        end
    %总体不相似性度量
    a=0.2/(0.1+exp(-(b-n0)/v));
    d=d1+0.2*d2;
    %更新隶属度矩阵
    U=gengxin(d); %c程序               
    [P,I1]=max(U1,[],2);
    [P,I]=max(U,[],2);
    R=(I-I1);        
    the=sum(R~=0)/(h*w);
end
%划分类别
[P,I2]=max(U,[],2);
G=reshape(I2,h,w);
figure,imshow(G,[]);