There are several color representations in computer science : the standard RGB, but also HSV, HSL, CIE XYZ, YCC, CIELAB, CIELUV, … It seems to me that most of the times, these representation try to approximate human vision (colors perceptually identical should have similar representations)
But what I want to know is which representation is the most “stable” when it comes to pictures. I have an object, let’s say a bottle of Coke, and I have thousands of pictures of this bottle, taken under very different circumstances (the main difference would be the how light or dark the picture is, but there’s orientation, etc…)
My question is : what color representation will empirically give me the most stable representation of the colors of the bottle? The “red” color of the label should not vary too much. Well, I’ll know it will vary, but I would like to know the most “stable” representation.
I’ve been taught that HSV is better than RGB for these kind of things, but I have no clue for the rest.
Edit (technical details) : I take a particular point of the bottle. I pick the corresponding pixels in a thousand pictures of this point. I now have a cloud of points, that depend on the representation. I want the representation that minimizes the “size” of this cloud, for example the one that minimizes the mean distance of the points of the cloud to its barycenter.
You might want to check out http://www.cs.harvard.edu/~sjg/papers/cspace.pdf, which proposes a new colorspace apparently designed to address this precise question.