Feb. 12, 2024, 6:35 a.m. | /u/Smart-Firefighter509

Data Science www.reddit.com

I have relatively uniformly colored images and I extracted colors using k-means. k means 1 showed the best results for my modeling purposes, k means 2 not so much, and with k-means 3 there ceased to be differences between some channels of samples.

Is this a reasonable approach and is it technically different from calculating the mean of all pixels in the image.

Can it be said that I took the mean of all pixels if the result is the …

clustering colors datascience differences image images k-means mean modeling pixels samples

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Encounter Data Management Professional

@ Humana | Work at Home - Kentucky

Pre-sales Manager (Data, Analytics & AI)

@ Databricks | Stockholm, Sweden

Lecturer / Senior Lecturer - Medical Imaging

@ Central Queensland University | Mackay, QLD, AU

Intern - Research Engineer

@ Plus | Santa Clara, CA