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I'd like to build a color difference metric that will be invariant to small changes in lighting conditions. In other words, I should be able to tell that a red shirt is not the same as a blue shirt, even if one image is lit differently (brighter / darker, outdoors / incandescent).

Moving to YUV space and ignoring the luminance component I can deal with changing brightness conditions by considering the metric: $$d = \sqrt{\Delta U^2 + \Delta V^2}$$ But this metric still gives large "distances" when different light sources are used, due to the change in chrominance along the Planckian locus.

My question is:

Is there a color difference metric that is invariant to temperature differences? in a sense, I'm looking for a color space in which one of the three axes is temperature. Does such a space exist?

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    $\begingroup$ The following may be related: stackoverflow.com/questions/10922467/… But a canonical answer would still be appreciated. $\endgroup$ – nbubis Jun 21 '13 at 3:47
  • $\begingroup$ Interesting question. I guess this is what auto white balance on a camera does. I think you would apply some kind of auto-white balance, then convert to YUV? Like camera auto white balance I think there would be issues with scenes where the range of colours is not-typical. $\endgroup$ – geometrikal Jun 22 '13 at 10:47
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Other than searching for a color space, why don't you just first apply a color correction and then get the hue space?

If illumination is an issue, you might consider an illumination invariant color correction: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.108.6859&rep=rep1&type=pdf

Then simple convert your image to HSV and get H channel.

Additionally, if you have some clue about what you are looking at (such as roads, football fields etc) then you might use that too. Such an algorithm is mentioned here: https://stackoverflow.com/questions/10922467/illumination-invariant-image

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