matlab编写的基于图像内容的相似度判别

clc close all clear %本程序基于图像的色彩特征和纹理特征值对图像进行相似度度量，分别采用HSV色彩空间取特征量和LBP算法取特征值来计算 %相似性度量采用欧氏距离来度量，参考度量方式为巴士系数计算法 %读取图像源文件，为方便计算和检验，请规定图像大小为512*512像素 image1=imread('test3.jpg'); image2=imread('test33.jpg'); %显示源图片 figure(1); set(figure(1),'NumberTitle','off','Name','图片显示') ; subplot(211),imshow(image1); subplot(212),imshow(image2); image1g = rgb2gray(image1); image2g = rgb2gray(image2); imagelbp=LBP(image1g); imagelbp1=LBP(image2g); %对图像进行处理，使之转化为HSV空间 [H1,S1,V1]=GetHSV(image1); [H2,S2,V2]=GetHSV(image2); %对图像H，S, V进行量化，结合人类对七种光源红橙黄绿青蓝紫的敏感度，将HSV分化为36维向量 %量化化函数为Quantificate [H1,S1,V1]=Quantificate(H1,S1,V1); [H2,S2,V2]=Quantificate(H2,S2,V2); %归一化处理图像使之转化为36维的灰度图像以便运算 % [m,n]=size(image1); % [M,N]=size(image2); % G=zeros(m,n); % G1=zeros(M,N); G=Normalize(H1,S1,V1); G1=Normalize(H2,S2,V2); COUNT1=count(G); COUNT2=count(G1); y=zeros(36,1); for pp=1:36 y(pp,1)=pp; end figure; set(figure(2),'NumberTitle','off','Name','色彩直方图') ; subplot(211),bar(y,COUNT1); subplot(212),bar(y,COUNT2); COUNT=COUNT1-COUNT2; od=COUNT.*COUNT; m=size(COUNT); odist=0; for i=1:m odist=odist+od(i,1); end odist=sqrt(odist); % [COUNT3,x]=imhist(imagelbp); % [COUNT4,x]=imhist(imagelbp1); temp = 0:255; imagelbpt = reshape(imagelbp,1,[]); %降维方便histc统计出现次数 imagelbp1t = reshape(imagelbp1,1,[]); [COUNT3]=histc(imagelbpt,temp); [COUNT4]=histc(imagelbp1t,temp); COUNT0=COUNT3-COUNT4; odlbp=COUNT0.*COUNT0; m=size(COUNT0); odistlbp=0; for i=1:m odistlbp=odistlbp+odlbp(i,1); end odistlbp=sqrt(odistlbp); figure; set(figure(3),'NumberTitle','off','Name','LBP处理') ; subplot(2,2,1);imshow(imagelbp,[]) subplot(2,2,2);imshow(imagelbp1,[]); subplot(2,2,3);hist(imagelbpt,temp); subplot(2,2,4);hist(imagelbp1t,temp); Odistance=sqrt(odist^2+odistlbp^2); Odistance=log(sqrt(Odistance))