| Re: KMean Clustering of RGB Images [message #53895] |
Wed, 09 May 2007 07:43 |
Mort Canty
Messages: 134
Registered: March 2003
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Senior Member |
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helaha@gmx.net schrieb:
> Hello Mort,
> yes, the order should not play any role, but nevertheless the
> clustering of an RGB image, implemented with the example code above
> showed a dependency and that's my problem. I can't imagine why the
> CLUSTER_WTS Algorithm is sensitive to the sequence order.
> The clustering results are very stable and I think really identical,
> if the algorithm is started several times without changing the data
> sequence. Changing the data sequence leads to a very distinct and yet
> again very stable clustering result. But why is there this dependency
> on data sequence?
>
> Many thanks,
> Helmut
>
Yes, I get the same effect. I had difficulty following your code so I
reduced it to essentials (I hope):
PRO Cluster_KMean_Image
ImageName = DIALOG_PICKFILE()
CD, FILE_DIRNAME(ImageName)
READ_JPEG, ImageName, RGBImage
Info = SIZE(RGBImage)
ImageSizeX = Info[2]
ImageSizeY = Info[3]
PixelN = ImageSizeX*ImageSizeY
; optional
; RGBImage = TRANSPOSE(RGBImage, [0, 2, 1])
print, CLUST_WTS(REFORM(RGBImage, 3, PixelN), $
N_CLUSTERS = 2, N_ITERATIONS = 20)
END
It just prints out the cluster means. On a small JPEG image they are
nicely reproducible. But they should also be the same whether the
TRANSPOSE function is called or not. And as you say, they aren't. I
hesitate to think that there is a bug in CLUSTER_WTS, but just now I
don't see an alternative explanation.
- Mort
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| Re: KMean Clustering of RGB Images [message #53901 is a reply to message #53895] |
Wed, 09 May 2007 05:08  |
helaha
Messages: 26
Registered: March 2004
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Junior Member |
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> The order in which the distances are calculated in each iteration
> clearly plays no role.
>
> Cheers
>
> Mort
Hello Mort,
yes, the order should not play any role, but nevertheless the
clustering of an RGB image, implemented with the example code above
showed a dependency and that's my problem. I can't imagine why the
CLUSTER_WTS Algorithm is sensitive to the sequence order.
The clustering results are very stable and I think really identical,
if the algorithm is started several times without changing the data
sequence. Changing the data sequence leads to a very distinct and yet
again very stable clustering result. But why is there this dependency
on data sequence?
Many thanks,
Helmut
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| Re: KMean Clustering of RGB Images [message #53902 is a reply to message #53901] |
Wed, 09 May 2007 04:36 |
Mort Canty
Messages: 134
Registered: March 2003
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Senior Member |
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helaha@gmx.net schrieb:
> Thank you Mort,
> concerning the "order of data points" it is necessary to think about
> the construction of the RGB Data space. The original RGB image is a 3
> dim. array e.g. [3,ImageSizeX, ImageSizeY]. The RGB Data space, which
> should be clustered concerning only the grey values and without
> consideration of the pixel locations, has two dimensions e.g. [3,
> NumberOfPixels ]. Therefore the individual image pixels are
> transformed in a sequence of RGB triple values ("data points"). The
> first triple values correspond to the pixel located at [3, 0, 0] e.g.
> the left bottom corner of the image. If the image is mirrored, rotated
> ore transposed before RGB space construction, then the first RGB
> triple value will be altered. Nevertheless all the individual image
> pixels were transformed into the RGB space, only the sequence is
> changed.
>
> Thanks,
> Helmut
>
>
Hi Helmut,
Now I see what you were getting at. But I think you're confusing things
a bit. K-means clustering takes place entirely in the feature space
(3-D RGB space in your case) and makes no reference to any ordering of
the pixels, spatial or otherwise. The Euclidean distances from each
pixel to each cluster mean are determined and the pixel is assigned to
the cluster for which the distance is smallest. Then the means are
recalculated and the procedure repeated until the means cease to change.
The order in which the distances are calculated in each iteration
clearly plays no role. The indeterminacy arises from the random
initialization of the cluster means.
Cheers
Mort
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| Re: KMean Clustering of RGB Images [message #53904 is a reply to message #53902] |
Wed, 09 May 2007 03:00 |
helaha
Messages: 26
Registered: March 2004
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Junior Member |
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> Not sure what you mean by "order of the data points". But any clustering
> algorithm that minimizes a cost function (like k-means) can get trapped
> in a local minimum.
Thank you Mort,
concerning the "order of data points" it is necessary to think about
the construction of the RGB Data space. The original RGB image is a 3
dim. array e.g. [3,ImageSizeX, ImageSizeY]. The RGB Data space, which
should be clustered concerning only the grey values and without
consideration of the pixel locations, has two dimensions e.g. [3,
NumberOfPixels ]. Therefore the individual image pixels are
transformed in a sequence of RGB triple values ("data points"). The
first triple values correspond to the pixel located at [3, 0, 0] e.g.
the left bottom corner of the image. If the image is mirrored, rotated
ore transposed before RGB space construction, then the first RGB
triple value will be altered. Nevertheless all the individual image
pixels were transformed into the RGB space, only the sequence is
changed.
Thanks,
Helmut
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| Re: KMean Clustering of RGB Images [message #53925 is a reply to message #53904] |
Tue, 08 May 2007 06:13 |
Mort Canty
Messages: 134
Registered: March 2003
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Senior Member |
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helaha@gmx.net schrieb:
> Recently I tested the Kmeans clustering of an RGB image with the
> CLUSTER and CLUST_WTR commands.
> The results were not very promising, because they depended on the
> image orientation and on the number of image pixels.
> I'm not a specialist in clustering, but in my opinion the results
> should be identical, especially regardless of the order of the data
> points.
>
Not sure what you mean by "order of the data points". But any clustering
algorithm that minimizes a cost function (like k-means) can get trapped
in a local minimum. The IDL help for CLUST_WTR says
"Because the initial clusters are chosen randomly, your results may
differ slightly each time the CLUST_WTS routine is invoked, even for the
same input data. For data with well-defined clusters the differences
should be slight. For randomly-scattered data (no distinguishable
clusters), the results may be significantly different, which may
indicate that k-means clustering is not appropriate for your data."
- Mort
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