Matlab）实现HSV非等间隔量化--相似判断：欧式距离--输出图片-

最新推荐文章于 2023-12-28 12:38:40 发布

转载最新推荐文章于 2023-12-28 12:38:40 发布 · 4.6k 阅读

本文介绍了一种利用HSV空间颜色直方图进行图像检索的方法，通过非等间隔量化HSV空间的颜色分量，并计算其直方图作为颜色特征，实现图像的高效匹配与检索。

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%**************************************************************************
%                 图像检索——提取颜色特征
%HSV空间颜色直方图(将RGB空间转化为HSV空间并进行非等间隔量化，
%将三个颜色分量表示成一维矢量，再计算其直方图作为颜色特征
%function : Hist = ColorHistogram(Image)
%Image    : 输入图像数据
%Hist     : 返回颜色直方图特征向量256维
%**************************************************************************
function Hist = ColorHistogram(Image)
Image = imread('D:\matlab\work\image_0007.jpg');
[M,N,O] = size(Image);
[h,s,v] = rgb2hsv(Image);
H = h; S = s; V = v;
h = h*360;

%将hsv空间非等间隔量化：
%  h量化成16级；
%  s量化成4级；
%  v量化成4级；
for i = 1:M
    for j = 1:N
        if h(i,j)<=15||h(i,j)>345
            H(i,j) = 0;
        end
        if h(i,j)<=25&&h(i,j)>15
            H(i,j) = 1;
        end
        if h(i,j)<=45&&h(i,j)>25
            H(i,j) = 2;
        end
        if h(i,j)<=55&&h(i,j)>45
            H(i,j) = 3;
        end
        if h(i,j)<=80&&h(i,j)>55
            H(i,j) = 4;
        end
        if h(i,j)<=108&&h(i,j)>80
            H(i,j) = 5;
        end
        if h(i,j)<=140&&h(i,j)>108
            H(i,j) = 6;
        end
        if h(i,j)<=165&&h(i,j)>140
            H(i,j) = 7;
        end
        if h(i,j)<=190&&h(i,j)>165
            H(i,j) = 8;
        end
        if h(i,j)<=220&&h(i,j)>190
            H(i,j) = 9;
        end
        if h(i,j)<=255&&h(i,j)>220
            H(i,j) = 10;
        end
        if h(i,j)<=275&&h(i,j)>255
            H(i,j) = 11;
        end
        if h(i,j)<=290&&h(i,j)>275
            H(i,j) = 12;
        end
        if h(i,j)<=316&&h(i,j)>290
            H(i,j) = 13;
        end
        if h(i,j)<=330&&h(i,j)>316
            H(i,j) = 14;
        end
        if h(i,j)<=345&&h(i,j)>330
            H(i,j) = 15;
        end
    end
end
for i = 1:M
    for j = 1:N
        if s(i,j)<=0.15&&s(i,j)>0
            S(i,j) = 0;
        end
        if s(i,j)<=0.4&&s(i,j)>0.15
            S(i,j) = 1;
        end
        if s(i,j)<=0.75&&s(i,j)>0.4
            S(i,j) = 2;
        end
        if s(i,j)<=1&&s(i,j)>0.75
            S(i,j) = 3;
        end
    end
end
for i = 1:M
    for j = 1:N
        if v(i,j)<=0.15&&v(i,j)>0
            V(i,j) = 0;
        end
        if v(i,j)<=0.4&&v(i,j)>0.15
            V(i,j) = 1;
        end
        if v(i,j)<=0.75&&v(i,j)>0.4
            V(i,j) = 2;
        end
        if v(i,j)<=1&&v(i,j)>0.75
            V(i,j) = 3;
        end
    end
end

%将三个颜色分量合成为一维特征向量：L = H*Qs*Qv+S*Qv+v；Qs,Qv分别是S和V的量化级数, L取值范围[0,255]
%取Qs = 4; Qv = 4
for  i = 1:M
    for j = 1:N
        L(i,j) = H(i,j)*16+S(i,j)*4+V(i,j);
    end
end
%计算L的直方图
for i = 0:255
    Hist(i+1) = size(find(L==i),1);
end
% Hist = Hist/sum(Hist);
T0=Hist;

% 循环读入图像
A=dir('D:\matlab\work\strawberry\*.jpg');
for k=1:size(A)
   B=strcat('\matlab\work\strawberry\',A(k).name);
   Image=imread(B);
%end
[M,N,O] = size(Image);
%M = 256;
%N = 256;

% 计算每一幅图像的颜色直方图

[h,s,v] = rgb2hsv(Image);
H = h; S = s; V = v;
h = h*360;

%将hsv空间非等间隔量化：
%  h量化成16级；
%  s量化成4级；
%  v量化成4级；
for i = 1:M
    for j = 1:N
        if h(i,j)<=15||h(i,j)>345
            H(i,j) = 0;
        end
        if h(i,j)<=25&&h(i,j)>15
            H(i,j) = 1;
        end
        if h(i,j)<=45&&h(i,j)>25
            H(i,j) = 2;
        end
        if h(i,j)<=55&&h(i,j)>45
            H(i,j) = 3;
        end
        if h(i,j)<=80&&h(i,j)>55
            H(i,j) = 4;
        end
        if h(i,j)<=108&&h(i,j)>80
            H(i,j) = 5;
        end
        if h(i,j)<=140&&h(i,j)>108
            H(i,j) = 6;
        end
        if h(i,j)<=165&&h(i,j)>140
            H(i,j) = 7;
        end
        if h(i,j)<=190&&h(i,j)>165
            H(i,j) = 8;
        end
        if h(i,j)<=220&&h(i,j)>190
            H(i,j) = 9;
        end
        if h(i,j)<=255&&h(i,j)>220
            H(i,j) = 10;
        end
        if h(i,j)<=275&&h(i,j)>255
            H(i,j) = 11;
        end
        if h(i,j)<=290&&h(i,j)>275
            H(i,j) = 12;
        end
        if h(i,j)<=316&&h(i,j)>290
            H(i,j) = 13;
        end
        if h(i,j)<=330&&h(i,j)>316
            H(i,j) = 14;
        end
        if h(i,j)<=345&&h(i,j)>330
            H(i,j) = 15;
        end
    end
end
for i = 1:M
    for j = 1:N
        if s(i,j)<=0.15&&s(i,j)>0
            S(i,j) = 0;
        end
        if s(i,j)<=0.4&&s(i,j)>0.15
            S(i,j) = 1;
        end
        if s(i,j)<=0.75&&s(i,j)>0.4
            S(i,j) = 2;
        end
        if s(i,j)<=1&&s(i,j)>0.75
            S(i,j) = 3;
        end
    end
end
for i = 1:M
    for j = 1:N
        if v(i,j)<=0.15&&v(i,j)>0
            V(i,j) = 0;
        end
        if v(i,j)<=0.4&&v(i,j)>0.15
            V(i,j) = 1;
        end
        if v(i,j)<=0.75&&v(i,j)>0.4
            V(i,j) = 2;
        end
        if v(i,j)<=1&&v(i,j)>0.75
            V(i,j) = 3;
        end
    end
end

%将三个颜色分量合成为一维特征向量：L = H*Qs*Qv+S*Qv+v；Qs,Qv分别是S和V的量化级数, L取值范围[0,255]
%取Qs = 4; Qv = 4
for  i = 1:M
    for j = 1:N
        L(i,j) = H(i,j)*16+S(i,j)*4+V(i,j);
    end
end
%计算L的直方图
for i = 0:255
    Hist(i+1) = size(find(L==i),1);
end
% Hist = Hist/sum(Hist);
T=Hist;

% 计算图像库中每一幅图像与查询例子图像的欧式距离
diff = T0-T;
EulerDistance = sqrt( sum( diff(:).*diff(:) ) );
w(k)=EulerDistance;
end

%--------------------------------------------------------------------------
%按颜色的相似性大小显示查询结果图片
%--------------------------------------------------------------------------
[B,IX]=sort(w,2);
A=dir('D:\matlab\work\strawberry\*.jpg');
for n=1:12
     subplot(3,4,n);
     I=imread(strcat('\matlab\work\strawberry\',A(IX(n)).name));
     imshow(I);
end