Memory vectors for similarity search in high-dimensional spaces

TL;DR

This paper introduces a memory vector-based indexing architecture for high-dimensional similarity search, significantly speeding up search times with minimal quality loss, especially effective in high-dimensional spaces.

Contribution

The paper proposes a novel memory vector architecture that improves search efficiency in high-dimensional spaces by summarizing database subsets with optimized representative vectors.

Findings

Faster search with minimal quality loss compared to exhaustive methods

Complexity reduction is more effective in high-dimensional spaces

Demonstrated practical utility in large-scale image search scenarios

Abstract

We study an indexing architecture to store and search in a database of high-dimensional vectors from the perspective of statistical signal processing and decision theory. This architecture is composed of several memory units, each of which summarizes a fraction of the database by a single representative vector. The potential similarity of the query to one of the vectors stored in the memory unit is gauged by a simple correlation with the memory unit's representative vector. This representative optimizes the test of the following hypothesis: the query is independent from any vector in the memory unit vs. the query is a simple perturbation of one of the stored vectors. Compared to exhaustive search, our approach finds the most similar database vectors significantly faster without a noticeable reduction in search quality. Interestingly, the reduction of complexity is provably better in high-dimensional spaces. We empirically demonstrate its practical interest in a large-scale image search scenario with off-the-shelf state-of-the-art descriptors.