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How code GMM RGB image segmentation in matlab?

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Steven Pranata
Steven Pranata 2019-11-29
评论： Fowzi barznji 2020-3-3
GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM
  2 个评论
Adam
Adam 2019-11-29
If you have the statistics toolbox then
doc gmdistribution
should help. If not then you can search the File Exchange or program it yourself.
Image Analyst
Image Analyst 2019-12-15
Original question:
GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM

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回答（1 个）

Fowzi barznji
Fowzi barznji 2020-3-3
Try this code
clc;
[file,path] = uigetfile('*.jpg');
disp(['User selected ', fullfile(path,file)]);
img=imread(fullfile(path,file));
EMSeg(img,3);
% you can change thne number of clusters (3) to another choice number
  2 个评论
Fowzi barznji
Fowzi barznji 2020-3-3
here the GMM Function u should use to call it
function [mask,mu,v,p]=EMSeg(ima,k)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Expectation Maximization image segmentation
%
% Input:
% ima: grey color image
% k: Number of classes
% Output:
% mask: clasification image mask
% mu: vector of class means
% v: vector of class variances
% p: vector of class proportions
%
% Example: [mask,mu,v,p]=EMSeg(image,3);
%
% Author: Prof. Jose Vicente Manjon Herrera
% Email: jmanjon@fis.upv.es
% Date: 02-05-2006
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% check image
ima=double(ima);
copy=ima; % make a copy
ima=ima(:); % vectorize ima
mi=min(ima); % deal with negative
ima=ima-mi+1; % and zero values
m=max(ima);
s=length(ima);
% create image histogram
h=histogram(ima);
x=find(h);
h=h(x);
x=x(:);h=h(:);
% initiate parameters
mu=(1:k)*m/(k+1);
v=ones(1,k)*m;
p=ones(1,k)*1/k;
% start process
sml = mean(diff(x))/1000;
while(1)
% Expectation
prb = distribution(mu,v,p,x);
scal = sum(prb,2)+eps;
loglik=sum(h.*log(scal));
%Maximizarion
for j=1:k
pp=h.*prb(:,j)./scal;
p(j) = sum(pp);
mu(j) = sum(x.*pp)/p(j);
vr = (x-mu(j));
v(j)=sum(vr.*vr.*pp)/p(j)+sml;
end
p = p + 1e-3;
p = p/sum(p);
% Exit condition
prb = distribution(mu,v,p,x);
scal = sum(prb,2)+eps;
nloglik=sum(h.*log(scal));
if((nloglik-loglik)<0.0001) break; end;
clf
plot(x,h);
hold on
plot(x,prb,'g--')
plot(x,sum(prb,2),'r')
drawnow
end
% calculate mask
mu=mu+mi-1; % recover real range
s=size(copy);
mask=zeros(s);
for i=1:s(1),
for j=1:s(2),
for n=1:k
c(n)=distribution(mu(n),v(n),p(n),copy(i,j));
end
a=find(c==max(c));
mask(i,j)=a(1);
end
end
function y=distribution(m,v,g,x)
x=x(:);
m=m(:);
v=v(:);
g=g(:);
for i=1:size(m,1)
d = x-m(i);
amp = g(i)/sqrt(2*pi*v(i));
y(:,i) = amp*exp(-0.5 * (d.*d)/v(i));
end
function[h]=histogram(datos)
datos=datos(:);
ind=find(isnan(datos)==1);
datos(ind)=0;
ind=find(isinf(datos)==1);
datos(ind)=0;
tam=length(datos);
m=ceil(max(datos))+1;
h=zeros(1,m);
for i=1:tam,
f=floor(datos(i));
if(f>0 & f<(m-1))
a2=datos(i)-f;
a1=1-a2;
h(f) =h(f) + a1;
h(f+1)=h(f+1)+ a2;
end;
end;
h=conv(h,[1,2,3,2,1]);
h=h(3:(length(h)-2));
h=h/sum(h);
Fowzi barznji
Fowzi barznji 2020-3-3
NOte that the Author: Prof. Jose Vicente Manjon Herrera

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