How code GMM RGB image segmentation in matlab?
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GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM
2 commentaires
Adam
le 29 Nov 2019
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
le 15 Déc 2019
Original question:
GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM
Réponses (1)
Fowzi barznji
le 3 Mar 2020
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 commentaires
Fowzi barznji
le 3 Mar 2020
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);
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