I wonder if it would be best to return multiple colors when something appears to have multiple primary ones - e.g. black and white for a zebra, green and red for a watermelon.

Separating the subject from the background would also be useful - as simple as weighting the center of the image more than the edges?

Hah. I was thinking the same thing: https://alexbeals.com/projects/colorize/search.php?q=rainbow

Depends on exactly what "average" means in this context.

The most naive of interpretations (e.g. averaging each of the R/G/B values) is generally not what we want.

Would be super cool to add some different averaging schemes to this.

I've done this exercise and generally what you want is a combination of your suggestion (which is elaborated by 'polyphora[0]) and 'p1necone's suggestion [1]

Polyphora mentioned CIELAB as just one example, and it's a good example. I believe state of the art these days is Oklab[2], talked about here[3]. I'd like to pull out a comment from 'jiggawatts in that discussion:

> This is a tour de force of colour theory, and should be mandatory reading for anyone serious about computer colour!

I completely agree.

With regards to 'p1necone's suggestion, k-nearest neighbors is one simple and relatively easy way to separate the colors into bins. I've only done this on a single image, but with multiple images maybe you could also k-nn bin the resulting colors from each image and only return bins which have multiple members.

0: https://news.ycombinator.com/item?id=25828733

1: https://news.ycombinator.com/item?id=25828773

2: https://bottosson.github.io/posts/oklab/

3: https://news.ycombinator.com/item?id=25525726