基于纹理的图像检索源代码

%基于共生矩阵的纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵 function T=Texture(Image) %Image=imread('5.JPG'); [M,N,O]=size(Image); % M = 256; % N = 256; %========================================================================== %将各颜色分量转化为灰度 Gray=rgb2gray(Image); %Gray=double(0.3*Image(:,:,1)+0.59*Image(:,:,2)+0.11*Image(:,:,3)); % imshow(uint8(Gray)) %========================================================================== %为了减少计算量，对原始图像灰度级压缩，将Gray量化成16级 for i=1:M for j=1:N for n=1:256/16 if (n-1)*16<=Gray(i,j)&&Gray(i,j)<=(n-1)*16+15 Gray(i,j)=n-1; end end end end %========================================================================== %计算四个共生矩阵P,取距离d=1，角度分别为0,45,90,135 P=zeros(16,16,4); for m=1:16 for n=1:16 for i=1:M for j=1:N %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if j<N&&Gray(i,j)==m-1&&Gray(i,j+1)==n-1 % 0 P(m,n,1)=P(m,n,1)+1; end if j>1&&Gray(i,j)==m-1&&Gray(i,j-1)==n-1 % 0 P(m,n,1)=P(m,n,1)+1; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if i<M&&j<N&&Gray(i,j)==m-1&&Gray(i+1,j+1)==n-1 P(m,n,2)=P(m,n,2)+1; % 45 end if i>1&&j>1&&Gray(i,j)==m-1&&Gray(i-1,j-1)==n-1 P(m,n,2)=P(m,n,2)+1; % 45 end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if i<M&&Gray(i,j)==m-1&&Gray(i+1,j)==n-1 P(m,n,3)=P(m,n,3)+1; % 90 end if i>1&&Gray(i,j)==m-1&&Gray(i-1,j)==n-1 P(m,n,3)=P(m,n,3)+1; % 90 end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if i<M&&j>1&&Gray(i,j)==m-1&&Gray(i+1,j-1)==n-1 P(m,n,4)=P(m,n,4)+1; % 135 end if i>1&&j<N&&Gray(i,j)==m-1&&Gray(i-1,j+1)==n-1 P(m,n,4)=P(m,n,4)+1; % 135 end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end end end end %========================================================================== % 对共生矩阵归一化 for n =1:4 P(:,:,n)=P(:,:,n)/sum(sum(P(:,:,n))); end %========================================================================== %对共生矩阵计算能量、熵、惯性矩、逆差矩、相关5个纹理参数 H=zeros(1,4); I=zeros(1,4); Ux=zeros(1,4); Uy=zeros(1,4); deltaX=zeros(1,4); deltaY=zeros(1,4); C=zeros(1,4); B=zeros(1,4); for n = 1:4 E(n)=sum(sum(P(:,:,n).^2)); %能量(角二阶矩) for i=1:16 for j=1:16 if P(i,j,n)~=0 H(n)=-P(i,j,n)*log(P(i,j,n))+H(n); %熵 end I(n)=(i-j)^2*P(i,j,n)+I(n); %惯性矩 B(n)=1/(1+(i-j)^2)*P(i,j,n)+B(n); %逆差矩 Ux(n)=i*P(i,j,n)+Ux(n); %相关性中μx Uy(n)=j*P(i,j,n)+Uy(n); %相关性中μy end end end for n=1:4 for i=1:16 for j=1:16 deltaX(n)=(i-Ux(n))^2*P(i,j,n)+deltaX(n); %相关性中σx deltaY(n)=(j-Uy(n))^2*P(i,j,n)+deltaY(n); %相关性中σy C(n)=i*j*P(i,j,n)+C(n); end end C(n)=(C(n)-Ux(n)*Uy(n))/deltaX(n)/deltaY(n); %相关 end %========================================================================== %求能量、熵、惯性矩、逆差矩、相关的标准差作为最终的纹理特征 % T(1)=mean(E); T(2)=mean(H); % T(3)=mean(I); T(4)=mean(C); T(1)=sqrt(cov(E)); T(2)=sqrt(cov(H)); T(3)=sqrt(cov(I)); T(4)=sqrt(cov(B)); T(5)=sqrt(cov(C));