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I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. Does anyone have a nice idea on how to reduce the noise a bit? anyone know some postprocessing for K-means. I would usually just apply a median filter to the image or something of the sort but I want to know if there is something a little nicer out there. Thank you in advanced. Not sure if posting this question here was the correct decision. Let me know.

p.s. this was all done in python by the way, only brightly colored pixels were clustered.

enter image description here

I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. Does anyone have a nice idea on how to reduce the noise a bit? anyone know some postprocessing for K-means. I would usually just apply a median filter to the image or something of the sort but I want to know if there is something a little nicer out there. Thank you in advanced. Not sure if posting this question here was the correct decision. Let me know.

p.s. this was all done in python by the way, only brightly colored pixels were clustered.

enter image description here

I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. Does anyone have a nice idea on how to reduce the noise a bit? anyone know some postprocessing for K-means. I would usually just apply a median filter to the image or something of the sort but I want to know if there is something a little nicer out there.

p.s. this was all done in python by the way, only brightly colored pixels were clustered.

enter image description here

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JEquihua
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I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. Does anyone have a nice idea on how to reduce the noise a bit? anyone know some postprocessing for K-means. I would usually just apply a median filter to the image or something of the sort but I want to know if there is something a little nicer out there. Thank you in advanced. Not sure if posting this question here was the correct decision. Let me know.

Julian.

p.s. this was all done in python by the way, only brightly colored pixels were clustered.

enter image description here

I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. Does anyone have a nice idea on how to reduce the noise a bit? anyone know some postprocessing for K-means. I would usually just apply a median filter to the image or something of the sort but I want to know if there is something a little nicer out there. Thank you in advanced. Not sure if posting this question here was the correct decision. Let me know.

Julian.

p.s. this was all done in python by the way, only brightly colored pixels were clustered.

enter image description here

I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. Does anyone have a nice idea on how to reduce the noise a bit? anyone know some postprocessing for K-means. I would usually just apply a median filter to the image or something of the sort but I want to know if there is something a little nicer out there. Thank you in advanced. Not sure if posting this question here was the correct decision. Let me know.

p.s. this was all done in python by the way, only brightly colored pixels were clustered.

enter image description here

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JEquihua
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  • 28
  • 48

Image Clustering with K-means - Postprocessing

I did some clustering on an image (each pixel is an observation that has 5 variables associated with it), I get pretty detailed results but they are a little bit noisey... I think. I used K-means. Does anyone have a nice idea on how to reduce the noise a bit? anyone know some postprocessing for K-means. I would usually just apply a median filter to the image or something of the sort but I want to know if there is something a little nicer out there. Thank you in advanced. Not sure if posting this question here was the correct decision. Let me know.

Julian.

p.s. this was all done in python by the way, only brightly colored pixels were clustered.

enter image description here