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.

本文提出了一种基于图遍历和压缩表征的方法，该方法编码可索引向量使用量化和利用图结构改进相似度估计。该方法在保证较小比较集的高精度及显著内存压缩的同时，在 64-128 字节每向量的操作点上，优于现有技术在二十亿规模公共基准测试中的表现。