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.


On Sat, Nov 21, 2015 at 11:28 AM, Hakim Benoudjit <h.benoudjit@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 :

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.


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