We are applying ML in creating images, we want to apply ML to the images to try and determine which colour certain customer clusters respond to.
The problem is that there are 16 million different colour possibilities when we apply OCR to the final image that is created.
Colours in Hex code are encode #000000 or #ffffff (black and white). But RM does not know that the RM company logo has primarily four colours #f5e44c, #e26937, #7a7d82 and #32353d. It does not even understand the distances between these colours and that #f5e44c and #e26937 would be nearest neighbours. It would not know to recommend using #ce6033 because it is so close to #e26937.
The calculation of the colour codes follows this methodology:
White RGB Color
White RGB code = 255*65536+255*256+255 = #FFFFFF
Blue RGB Color
Blue RGB code = 0*65536+0*256+255 = #0000FF
Red RGB Color
Red RGB code = 255*65536+0*256+0 = #FF0000
Green RGB Color
Green RGB code = 0*65536+255*256+0 = #00FF00
Gray RGB Color
Gray RGB code = 128*65536+128*256+128 = #808080
Yellow RGB Color
Yellow RGB code = 255*65536+255*256+0 = #FFFF00
Has anyone used RM to reduce the number of colours used in ML by applying K Nearest Neighbour? I want to reduce that 16 million down to a much more usable number of around 117 colours.