Build a classifier to separate images of the four seasons, based on the dominant colors available in images of each season, using the new built-in region operations.
Some public domain images are the source of the seasons' colors.
The images have different color distributions, but describing them is not easy in the RGB space.
In the Lab space, the hues are easily separable from the luminance.
A convex hull is defined from the distribution of the dominant colors for each season with the function ConvexHullMesh.
Define a distance function from a set of colors to a region using the new RegionDistance function.
The predicted season is the one that minimizes this distance.
Test the classifier on the remaining images.
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