Derived Codebooks for High-Accuracy Nearest Neighbor Search

TL;DR

This paper introduces a novel method using derived codebooks and a two-pass search to enable high-accuracy 16-bit quantization in nearest neighbor search without sacrificing response time, significantly improving accuracy over traditional 8-bit methods.

Contribution

The paper proposes a new approach that combines derived codebooks with a two-pass search to achieve 16-bit quantization accuracy at the speed of 8-bit methods.

Findings

Achieves Recall@100 of 0.85 in 5.2 ms on 1 billion vectors.

Outperforms standard 16-bit quantizers, which take 39 ms for the same recall.

Maintains near 8-bit response times while boosting accuracy.

Abstract

High-dimensional Nearest Neighbor (NN) search is central in multimedia search systems. Product Quantization (PQ) is a widespread NN search technique which has a high performance and good scalability. PQ compresses high-dimensional vectors into compact codes thanks to a combination of quantizers. Large databases can, therefore, be stored entirely in RAM, enabling fast responses to NN queries. In almost all cases, PQ uses 8-bit quantizers as they offer low response times. In this paper, we advocate the use of 16-bit quantizers. Compared to 8-bit quantizers, 16-bit quantizers boost accuracy but they increase response time by a factor of 3 to 10. We propose a novel approach that allows 16-bit quantizers to offer the same response time as 8-bit quantizers, while still providing a boost of accuracy. Our approach builds on two key ideas: (i) the construction of derived codebooks that allow a fast and approximate distance evaluation, and (ii) a two-pass NN search procedure which builds a candidate set using the derived codebooks, and then refines it using 16-bit quantizers. On 1 billion SIFT vectors, with an inverted index, our approach offers a Recall@100 of 0.85 in 5.2 ms. By contrast, 16-bit quantizers alone offer a Recall@100 of 0.85 in 39 ms, and 8-bit quantizers a Recall@100 of 0.82 in 3.8 ms.