Sliced Wasserstein embeddings + HNSW for color-scheme search over 48k paintings [P]

Key points

• Each image is reduced to 128 colors of equal weight, so the natural distance is the Earth Mover's Distance, which is too slow for nearest-neighbor search over 48,695 items (about 6 ms per pair).1
• L1 between two vectors is the 1D W1 averaged over the directions, a lower bound on the true W1.1
• soft color histogram, Hellinger: 45%, 13.5%1
• The index is HNSW written from scratch (M=16, efConstruction=200): recall@10 of 1.000 at ef=32, 0.25 ms against 14 ms for brute force.1

Sources (1)

• [1]Sliced Wasserstein embeddings + HNSW for color-scheme search over 48k paintings [P]
  r/MachineLearning (top, daily) · Oct 11, 02:39 PM

  The embedding: project the colors (OKLab) onto 8 directions on a Fibonacci hemisphere, sort, and average into 16 quantiles.

  Each image is reduced to 128 colors of equal weight, so the natural distance is the Earth Mover's Distance, which is too slow for nearest-neighbor search over 48,695 items (about 6 ms per pair).

Extractive summary: sentences quoted from the sources.