| Fuzzy classification [message #39336] |
Fri, 14 May 2004 08:34  |
joao.antunes
Messages: 4
Registered: May 2004
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Junior Member |
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Hello,
I am looking for an IDL implementation of the fuzzy c-means (FCM)
clustering algorithm and fuzzy maximum likelihood algorithm (FML) to
classication of satellite images. The resulted cluster centers
obtained by the FCM algorithm from the first step are input into the
FML algorithm.
Any help would be greatly appreciated.
Thanks in advance.
Joao.
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| Re: Fuzzy classification [message #39449 is a reply to message #39336] |
Tue, 18 May 2004 05:08  |
Vincenzo Positano
Messages: 2
Registered: January 2001
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Junior Member |
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"Joao F." <joao.antunes@agr.unicamp.br> ha scritto nel messaggio
news:7568d93c.0405140734.5b3e4df4@posting.google.com...
> Hello,
>
> I am looking for an IDL implementation of the fuzzy c-means (FCM)
> clustering algorithm and fuzzy maximum likelihood algorithm (FML) to
> classication of satellite images. The resulted cluster centers
> obtained by the FCM algorithm from the first step are input into the
> FML algorithm.
>
This is an implementation of FCM for image segmentation.
mu initialization should be adapted to your problem.
The function seems to work on medical images with 3-4
classes.
Vincenzo Positano
;image segmentation based on fuzzy clustering
;
; image input image
; num_class number of classes
; m fuzzy factor
; eps threeshold
; n_max max iteration number
; a cluster centers
function fuzzy_clustering,image,num_class,m,eps,n_max,CENTER=a
sz=SIZE(image,/DIMENSIONS)
x = REFORM(image,sz[0]*sz[1])
n=LONG(N_ELEMENTS(x))
c=num_class
a=DBLARR(c)
mu=DBLARR(c,n)
kk=0
;mu initialization
idx=WHERE(image le 20)
mu[0,idx]=1.0
idx=WHERE((image ge 20) and (image lt 200))
mu[1,idx]=1.0
idx=WHERE(image ge 200)
mu[2,idx]=1.0
mu_old=DBLARR(c,n)
me=(2/(m-1.0))
while (kk le n_max) do begin
for i=0L,c-1 do begin
tmp=REFORM(mu[i,*],n)
num = tmp^m*x
den = tmp^m
a[i]=TOTAL(num)/TOTAL(den)
endfor
for i=0L,c-1 do begin
num = DBLARR(c,n)
den = DBLARR(c,n)
num[0,*]=(ABS(x-a[i]))^me
for k=0L,c-1 do begin
den[k,*]=(ABS(x-a[k]))^me
num[k,*]=num[0,*]
endfor
pippo=TOTAL(num/den,1)
mu[i,*]=1.0/pippo
endfor
diff = TOTAL(ABS(mu-mu_old))/FLOAT(n)
if (diff le eps) THEN KK=n_max
mu_old=mu
kk=kk+1
endwhile
return,mu
end
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