Instead of taking the nearest candidates to , we can look for a set of candidates whose centroid is close to . The N-convex algorithm works by finding the closest colour to a given target colour for iterations, where the target is first initialised to be equal to the input pixel. Every iteration the closest colour added to the candidate list, and the quantisation error between it and the original input pixel is added to the target.
For well-distributed points, nearest neighbor search is often near O(logn)O(\log n)O(logn) in practice. In the worst case (all points clustered tightly or along a line), it can degrade to O(n)O(n)O(n), but this is uncommon with typical spatial data.
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