How code GMM RGB image segmentation in matlab?

GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM

[Adam]

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]

Original question:

GMM using Covariance and not grayscale image 1 D... I want use RGB image using GMM

Answer 1

[Fowzi barznji]

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

[Fowzi barznji]

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: [e-mail]

% 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]

NOte that the Author: Prof. Jose Vicente Manjon Herrera