基于k-means的图像分割MATLAB程序

close all

clear

I_rgb = imread('color-cam4-f0.bmp') %读取文件数据

figure(1)

subplot(1,2,1)

imshow(I_rgb) %显示原图

title('原始图像')

%将彩耐行色图像从昌姿哗RGB转化到lab彩色空间

C = makecform('srgb2lab') %设置转册衡换格式

I_lab = applycform(I_rgb, C)

%进行K-mean聚类将图像分割成3个区域

ab = double(I_lab(:,:,2:3)) %取出lab空间的a分量和b分量

nrows = size(ab,1)

ncols = size(ab,2)

ab = reshape(ab,nrows*ncols,2)

nColors = 4 %分割的区域个数为

[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',100) %重复聚类3次

pixel_labels = reshape(cluster_idx,nrows,ncols)

figure(1)

subplot(1,2,2)

imshow(pixel_labels,[]), title('聚类结果')

%显示分割后的各个区域

segmented_images = cell(1,nColors)

rgb_label = repmat(pixel_labels,[1 1 3])

for k = 1:nColors

color = I_rgb

color(rgb_label ~= k) = 0

segmented_images{k} = color

end

for i=1:nColors

figure(2),subplot(1,nColors,i)imshow(segmented_images{i}), title('分割结果')

end

function [mu,mask]=kmeans(ima,k)%k为指定类别数

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%

% kmeans image segmentation

%

% Input:

% ima: grey color image灰度图像

% k: Number of classes指定的图像中类别数目

% Output:

% mu: vector of class means 每个类的均值

% mask: clasification image mask分类后的图像掩膜（mask)

%

% Author: Jose Vicente Manjon Herrera

%Email: jmanjon@fis.upv.es

% Date: 27-08-2005

%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% check image

ima=double(ima)

copy=ima% make a copy

ima=ima(:) % vectorize ima将图像向量化，即一维化。

mi=min(ima) % deal with negative

ima=ima-mi+1% and zero values

s=length(ima)%获得图像像素个数

% create image histogram%创建图像直方图

m=max(ima)+1%最大像素值加1

h=zeros(1,m)%直方图，有m个bin

hc=zeros(1,m)%标号矩阵，每个像素点的值为该点所隶属的类别号

for i=1:s%s是图像象素个数，即考查每个像素

if(ima(i)>0) h(ima(i))=h(ima(i))+1end%直方图中对应bin加1

end

ind=find(h)%找到直方图中不为零的那些bin的序号。

hl=length(ind)%直方图中非零bin的个数

% initiate centroids

mu=(1:k)*m/(k+1)%k为指定的类别数，mu为不同类的分割点，相当于坐标轴上的整点

% start process

while(true)

oldmu=mu

% current classification

for i=1:hl

c=abs(ind(i)-mu)%就是相当于考察ind(i)在坐标轴上离哪个整点最近！注意mu总共就k个

cc=find(c==min(c))%cc保留距离ind(i)最近整点的前槐序号，序号为基悔羡1、2、3...k

hc(ind(i))=cc(1)

end

%recalculation of means 下面的程序用于计算每一类的均值位置

for i=1:k,

a=find(hc==i)

mu(i)=sum(a.*h(a))/sum(h(a))%h为直搏拍方图

end

if(mu==oldmu) breakend%循环结束条件

end

% calculate mask

s=size(copy)

mask=zeros(s)

mask1=mask%增加一个显示矩阵

size(mask1)

for i=1:s(1),

for j=1:s(2),

c=abs(copy(i,j)-mu)

a=find(c==min(c))

mask(i,j)=a(1)

end

end

mu=mu+mi-1 % recover real range

for i = 1 : k

p=find(mask==i)

mask1(p)=1/k*i

end

figure,imshow(mask1)

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