【图像分割】基于matlab直觉模糊C均值聚类图像分割IFCM【含Matlab源码 120期】

一、图像分割简介

理论知识参考：【基础教程】基于matlab图像处理图像分割【含Matlab源码 191期】

二、部分源代码

clear
close all
clc
clear
close all
clc
%% %%%%%%%%%%%%%%%图像%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 I=imread('3096.jpg');

if size(I,3) == 3
   I=rgb2gray(I);
else
end
figure;imshow(I);title('(a)原始图像');imwrite(I,'1.tiff','tiff','Resolution',300);%保存为tif
%I=I;%不加噪声
% I=imnoise(I,'speckle',deta_2);
I=imnoise(I,'salt & pepper',0.05); %加噪图椒盐噪声
% I=imnoise(I,'gaussian',0,0.01); % 加高斯噪声

 
figure;imshow(I);title('(b)加噪图像');imwrite(I,['2.','.tiff'],'tiff','Resolution',300);%保存为tif
[m,n]=size(I);

% k=4;
k=2;
r=3;
m_index=2;
beta=6;
neighbor=3;%3*3邻域去噪
lamda_s_enfcm=3;%s参数

I=im2double(I);
I4 = I(:);  %% 将图像灰度按列排列

%% ------------------------ ifcm------------------------
ifcm_label=zeros(m*n,1);
t=cputime;
tic;
[O2, U2, obj_fcn2] = ifcm(I4, k);
toc;
time_fcm_spatial_mean=cputime-t;
maxU2 = max(U2);   %隶属度最大  
for j=1:k
    index = find(U2(j, :) == maxU2);  %隶属度最大对应的像素位置
   ifcm_label(index) = j;    
end
labels2=reshape(ifcm_label,[m n]);


%显示聚类分割图
labels2(find(labels2==1))=0;
labels2(find(labels2==2))=255;
labels2(find(labels2==3))=125;
labels2(find(labels2==4))=180;
labels2=uint8(labels2);
% figure;imshow(labels2,[]);title('(c)聚类分割图');imwrite(labels2,'3.jpg');imwrite(labels2,'33.png')%保存为jpg
figure;imshow(labels2,[]);title('(c)聚类分割图');
imwrite(labels2,['3.1.','.tiff'],'tiff','Resolution',300);%保存为tif

function [center, U, obj_fcn] = ifcm(data,cluster_n)


if nargin ~= 2 & nargin ~= 3,
	error('Too many or too few input arguments!');
end

data_n = size(data, 1);
in_n = size(data, 2);

% Change the following to set default options
default_options = [2;	% exponent for the partition matrix U
		100;	% max. number of iteration
		1e-5;	% min. amount of improvement
		1];	% info display during iteration 

if nargin == 2,
	options = default_options;
else
	% If "options" is not fully specified, pad it with default values.
	if length(options) < 4,
		tmp = default_options;
		tmp(1:length(options)) = options;
		options = tmp;
	end
	% If some entries of "options" are nan's, replace them with defaults.
	nan_index = find(isnan(options)==1);
	options(nan_index) = default_options(nan_index);
	if options(1) <= 1,
		error('The exponent should be greater than 1!');
	end
end

expo = options(1);		% Exponent for U
max_iter = options(2);		% Max. iteration
min_impro = options(3);		% Min. improvement
display = options(4);		% Display info or not
obj_fcn = zeros(max_iter, 1);	% Array for objective function

%U = initfcm(cluster_n, data_n);			% Initial fuzzy partition
center= initifcmv(cluster_n);            %初始化聚类中心
[Miuij,Vij,Paiij]=initifcmmvp(data);

for i = 1:max_iter,
    [U, center, obj_fcn(i)] = ifcm_step(data, center, cluster_n, expo, Miuij,Vij,Paiij);
	if display, 
		fprintf('Iteration count = %d, obj. fcn = %f\n', i, obj_fcn(i));
	end
	% check termination condition
	if i>1
% 		if abs(obj_fcn(i) - obj_fcn(i-1)) < min_impro, break; end,
 %        if norm(old_U-U,2) < min_impro, break; end,
%        if norm(old_U-U,'fro') < min_impro, break; end,
       if norm(old_center-center,2) < min_impro, break; end,
       % (old_center-center).^2
         
    end
    old_U = U;
    old_center = center;
end

% iter_n = i;	% Actual number of iterations 
% % obj_fcn(iter_n+1:max_iter) = [];
% mf = old_U.^expo;       % 隶属度矩阵进行指数运算结果
% dist = distfcm(old_center, data);       % 计算距离矩阵
% obj_fcn = sum(sum((dist.^2).*mf));  % 计算目标函数值 


  

 

  
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三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 蔡利梅.MATLAB图像处理——理论、算法与实例分析[M].清华大学出版社，2020.
[2]杨丹,赵海滨,龙哲.MATLAB图像处理实例详解[M].清华大学出版社，2013.
[3]周品.MATLAB图像处理与图形用户界面设计[M].清华大学出版社，2013.
[4]刘成龙.精通MATLAB图像处理[M].清华大学出版社，2015.
[5]赵勇,方宗德,庞辉,王侃伟.基于量子粒子群优化算法的最小交叉熵多阈值图像分割[J].计算机应用研究. 2008,(04)

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原文链接：qq912100926.blog.csdn.net/article/details/112907955