HSV closer resembles physical properties, for most natural things. Hue and saturation variations are usually meaningful variations in the actual material. Brightness variations often end up being mostly about lighting, rather than the material. It can be surprisingly effective for simple segmentation [1], which is why it's usually the first one implemented in computer vision classes.
Our eyes have RGB sensors, but I would claim I perceive the colors in my surroundings in something like HSV (although, that could very well be from the way I learned colors). And, I think this makes sense: if you're looking for something, you want a color perception that's not overly sensitive to lighting conditions. RGB is directly related.
The segmentation aspect is interesting, but the problem I have with H is that it is circular, i.e. 0 and 1 represent virtually the same hue, and my intuition is that this lends itself poorly to a NN. The luminosity argument is valid, but that is not unique to HSL, hence my intuition that YCbCr (or related) would outperform.
HSV closer resembles physical properties, for most natural things. Hue and saturation variations are usually meaningful variations in the actual material. Brightness variations often end up being mostly about lighting, rather than the material. It can be surprisingly effective for simple segmentation [1], which is why it's usually the first one implemented in computer vision classes.
Our eyes have RGB sensors, but I would claim I perceive the colors in my surroundings in something like HSV (although, that could very well be from the way I learned colors). And, I think this makes sense: if you're looking for something, you want a color perception that's not overly sensitive to lighting conditions. RGB is directly related.
[1] https://medium.com/neurosapiens/segmentation-and-classificat...