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

Hakim Benoudjit h.benoudjit at gmail.com
Wed Nov 25 10:50:38 EST 2015


Thanks Emma, that's exactely what I was looking for.

Le mercredi 25 novembre 2015 12:02:34 UTC, Emmanuelle Gouillart a écrit :
>
> Hi Hakim, 
>
> I think this example from the gallery does what you want: merging slic 
> superpixels. 
>
> http://scikit-image.org/docs/dev/auto_examples/plot_rag_merge.html#example-plot-rag-merge-py 
>
> Best, 
> Emma 
>
> On Wed, Nov 25, 2015 at 03:50:08AM -0800, Hakim Benoudjit wrote: 
> > Hi Stéfan, 
>
> > Thanks for the suggestion. 
> > I didn't try SLIC algorithm yet, but it seems to me (from the resulting 
> images) 
> > that it segment images into small regions (superpixels) that could 
> belong 
> > (visually) to the same object. 
> > In my case (ideally), these superpixels need to be merged afterwards. 
> > Do you have an idea on how to achieve the subsequent merging? 
>
> > Le mercredi 25 novembre 2015 02:21:00 UTC, stefanv a écrit : 
>
> >     On 2015-11-24 01:47:00, Hakim Benoudjit <h.ben... at gmail.com> wrote: 
> >     > Thanks Juan, I think you're right. 
>
> >     > I might have to read the paper on SLIC algorithm to understand how 
> to 
> >     tune 
> >     > the "compactness" parameter. 
>
> >     You can also use SLIC to label the image, and then compute features 
> of 
> >     each SLIC region.  Or perhaps that is what is being suggested 
> already, I 
> >     wasn't sure. 
>
> >     Stéfan 
>
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