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I could be wrong on this, but we learned about K-means clustering this year in college, as far as I know, the K random exemplars which you use for K-means clust
by Newky 15y ago
I could be wrong on this, but we learned about K-means clustering this year in college, as far as I know, the K random exemplars which you use for K-means clusters obviously reduces the number of colors in the image to a certain N ( N <= K). This would mean that any live manipulation of the K colors would actually only modify the image which consists of only N colors.
The net result would be a live image like you suggest, but one with much less detail. Still very interesting though.
- tylerhobbs 15y agoK-means is only grouping all of the pixels in to K disjoint clusters, not actually altering the value of the pixels. Taking an entire cluster of pixels and shifting their values in the RGB space could produce some very interesting images. For example, you could easily make all of the almost-white pixels slightly blue. There would be no loss of variety among the pixels, they would just all have their blue values bumped up.
- gburt 15y agoYou compute 'centroids' which denote the centers of the clusters, but you don't have to change the values of all points to the centers of the clusters. In other words, you can maintain the detail in RGB space (as this author has) while reorganizing things in location space by their k-means clusters.
- Newky 15y agoI understand this, but does this mean that it is bi-directional in that a change to one of the palettes would reflect in the image. This is what I understood from the comment. If so, how does this work? Sorry for any misinformation in my comment