You mention a lookup table would have been tedious, but surely an easier method.
This might be a case of "to a hammer everything looks like a nail".
The solution in my familiarity would be to align the emoji in a grid, take one screenshot and write a script to iterate over the pixel data to figure out the most dominant color and how dominant. So the build of the lookup set would be automatic.
Less interesting for sure but easier (at least for a JS dayjob skillset).
Similar to a below comment, I know this isn't the point. Thanks for an interesting write up
I think a (N, D) array can be used as a lookup table.
Here, N is the number of emojies and D is the dimensionality of the color space (say 3 for RGB).
Each emoji is converted to a D-dimensional vector by computing the dominant color.
Then, we can take a D-dimensional vector from an input image and compute the closest emoji by using cosine similarity.
To minimize the loss function, we can increase the number of dominant colors, so the table would be (N, DxM).
In this case, each emoji is represented by M D-dimensional vectors, each one encoding a dominant color.
This might be a case of "to a hammer everything looks like a nail".
The solution in my familiarity would be to align the emoji in a grid, take one screenshot and write a script to iterate over the pixel data to figure out the most dominant color and how dominant. So the build of the lookup set would be automatic.
Less interesting for sure but easier (at least for a JS dayjob skillset).
Similar to a below comment, I know this isn't the point. Thanks for an interesting write up