Abstract
Similarity search approaches based on graph walks have
recently attained outstanding speed-accuracy trade-offs,
taking aside the memory requirements. In this paper, we
revisit these approaches by considering, additionally, the
memory constraint required to index billions of images on
a single server. This leads us to propose a method based
both on graph traversal and compact representations. We
encode the indexed vectors using quantization and exploit
the graph structure to refine the similarity estimation.
In essence, our method takes the best of these two
worlds: the search strategy is based on nested graphs,
thereby providing high precision with a relatively small set
of comparisons. At the same time it offers a significant
memory compression. As a result, our approach outperforms the state of the art on operating points considering
64–128 bytes per vector, as demonstrated by our results on
two billion-scale public benchmarks