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

[Hakim Benoudjit]

Hi Jonas,

Thanks for your response.
That's exactly what I've tried this week-end, by adding the (x, y) to 
gray-level intensity and giving the matrix of 3-components vector as input 
to k-means.
As for the normalization, I applied this formula to each column (intensity, 
x, y): (value - mean) / std_dev.
But, even with this normalization step, adding the (x, y) coordinates will 
influence the pixels on the left (resp. right) to be grouped together (See 
http://imgur.com/HxfkRig and original image taken from 
http://uk.mathworks.com/help/images/texture-segmentation-using-gabor-filters.html?refresh=true).

Maybe I will need to find another normalization to apply of the (intensity, 
x, y) space.

Le lundi 23 novembre 2015 23:53:34 UTC, Jonas Wulff a écrit :
>
> Hi Hakim,
>
> Have you tried just adding the coordinates of a pixel to its features? For 
> each pixel, the features would then be R,G,B,X,Y. From your description, 
> that seems what you're looking for.
>
> So if you have an RGB image I (so that I.shape = (height,width,3)), you 
> can do:
>
> y,x = np.mgrid[:height,:width]
> I_stacked = np.dstack((I,x,y))
> data = I_stacked.reshape((-1,5))
>
> ... and then use "data" as input to your clustering algorithm.
>
> You might want to scale / normalize the coordinates to fit the general 
> range of your color values -- but in general, this should do what I think 
> you're looking for.
>
> Cheers,
> -Jonas
>
>
>
>
>
> On Sat, Nov 21, 2015 at 2:23 AM, Hakim Benoudjit <h.ben... at gmail.com 
> <javascript:>> wrote:
>
>> 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> 
>>> 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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