[msclrhd]

That's interesting. So what you have is some sort of similarity metric that is colour based. You could define it as being 1 for an exact match, and 0 for not matching, so cyan, etc. would be 0 red, while orange may be slightly red (e.g. 0.1), and pink may be slightly more red (e.g. 0.25). This would be a 3D function/surface.

This would allow you to model classes of colours, like purples. Shades of a colour could then be any similar colour in the range 0 to 0.5 (or 0.25).

[roel_v]

Yes, this is what Delta E in the CIELAB color space is meant for. But in RGB color space this concept doesn't make sense; well, only if you would accept a non linear similarity metric - but then what is the metric for, if you need to apply a curve to it to normalize it? But still, this is different from modeling 'shades' of a color (if we say 'shades' are varying brightness in RGB space using the 0.21/0.72/0.7 brightness conversion - but again, I argue that 'shades' is an ambiguous concept).

My point is, again, that colors are a much more difficult concept than some in this discussion are making it out to be, and 'distance between colors' is more complex still.

[eru]

Yes, agreed.