Clustering of an image by taking into account the spatial context of each pixel (besides its intensity)

Hakim Benoudjit h.benoudjit at gmail.com
Fri Nov 20 20:23:27 EST 2015


Hi Juan,

Thanks for your answer, this seems to be a nice algorithm for the denoising 
of speckle.
But actually I'm looking for an image clustering (segmentation) technique 
instead (that would take into consideration the spatial context of pixels).

Le samedi 21 novembre 2015 00:47:21 UTC, Juan Nunez-Iglesias a écrit :
>
> Hey Hakim,
>
> The right answer here depends on your ultimate goal. If you're after 
> denoising, non-local means denoising (recently added to skimage) sounds 
> like exactly what you're after.
>
> Juan.
>
> On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.ben... at gmail.com 
> <javascript:>> wrote:
>
>> Hi Stéfan,
>>
>> Thanks for your reponse.
>> What I'm looking for is a *spatial criteria* that encourages the *clustering 
>> algorithm* (K-means or others) to group together similar *neighbouring 
>> pixels* inside the same cluster. This will help avoid having persistent 
>> noise inside a cluster. 
>>
>> Le vendredi 20 novembre 2015 13:20:15 UTC, Hakim Benoudjit a écrit :
>>>
>>> Hi,
>>>
>>> Is there a clustering algorithm implemented in *scikit-image *that 
>>> perform the image clustering by taking into account the *spatial 
>>> context *of the clustered pixel (its neighbourhood), besides its *pixel 
>>> brightness*?
>>>
>>> For the time being, I'm clustering images by reshaping them as vectors 
>>> of pixels intensities distributions, and then performing the *K-means *or 
>>> *Gaussian mixture models* implemented in *scikit-learn*. But, I'm 
>>> looking for a image clustering technique implemented (or could be 
>>> implemented) in *scikit-image *that would consider the neighbourhood of 
>>> a pixel when classifying it.
>>>
>>> Thanks.
>>>
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