K-means 处理 RGB 图像 ...-优快云博客

本文介绍了一种使用Matlab实现的图像压缩方法，该方法通过K-means聚类算法对图像的RGB通道进行压缩。首先将图像分解为三个独立的通道，并对每个通道应用K-means算法来减少颜色数量，从而达到压缩的目的。文中详细展示了Matlab代码实现过程及最终的压缩效果。

代码：

clear all;
close all;
clc;
im = imread('yy.jpg');
imr = im(:,:,1);
img = im(:,:,2);
imb = im(:,:,3);
[m,n] = size(imr);
% set the parameters
k = 4;
tic;
[mur,outlabelr,Jr] = dckmeans(imr,k,40);
[mug,outlabelg,Jg] = dckmeans(img,k,40);
[mub,outlabelb,Jb] = dckmeans(imb,k,40);
toc;
% get mu
mu = [mur;
    mug;
    mub];
for i = 1:m
    for j = 1:n
        outim(i,j,1) = mu(1,outlabelb(i,j));
        outim(i,j,2) = mu(2,outlabelb(i,j));
        outim(i,j,3) = mu(3,outlabelb(i,j));
    end
end
figure, imshow(im);
figure, imshow(uint8(outim));

其中dckmeans为：

function [mu,outlabel,J] = dckmeans(im,k,N)

copyim = im;
%
im = im + 1;
% set the parameters
[m,n] = size(im);
% initialization
mu = zeros(1,k);
for i = 1 : k
    mu(i) = 255./i;
end
h = zeros(1,max(im(:)));
y = zeros(1,max(im(:)));
l = length(h);
for i= 1:m
    for j=1:n
        h(im(i,j)) = h(im(i,j))+1;
    end
end
% start the iteration
for r = 1:N
    for i = 1:l
        for j = 1:k
            delta(j) = abs( i - mu(j));
        end
            minloc = find(delta == min(delta));
            y(i) = minloc(1);
    end
    %
    for j = 1:k
        a = find(y==j);
        mu(j) = sum(a.*h(a))./sum(h(a));
    end
    %
    J(r) = 0;
    for i = 1:l
        J(r) =  J(r) + abs( i - mu(y(i))).*h(i);
    end
    %if(J(r)-J(r-1)<J(r)*0.000001)
    %    break;
    %end
end
figure,plot(1:length(J),J);
% Output
outlabel= zeros(m,n);
for i =1:m
    for j= 1:n
        outlabel(i,j) = y(copyim(i,j)+1);
    end
end

转载于:https://www.cnblogs.com/helay/p/7133957.html