Online Product Quantization

TL;DR

This paper introduces an online product quantization method that incrementally updates the codebook for approximate nearest neighbor search in dynamic, large-scale databases, improving efficiency and adaptability.

Contribution

It proposes a novel online PQ model with partial codebook updates and a sliding window mechanism, enabling efficient real-time ANN search in evolving data environments.

Findings

Online PQ outperforms baseline methods in time efficiency.

Partial codebook updates reduce computational costs.

Model guarantees performance via a derived loss bound.

Abstract

Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behaviour of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.