Variable-Length Hashing

TL;DR

This paper introduces a lossless variable-length hashing method that enhances storage and search efficiency for large-scale similarity search by converting fixed-length codes into variable-length codes and employing multiple hash tables.

Contribution

It proposes a novel lossless variable-length hashing (VLH) technique that improves retrieval performance without increasing storage or search complexity, including a specific B-KMH method.

Findings

VLH achieves better retrieval performance with minimal storage increase.

B-KMH significantly improves retrieval accuracy.

The method maintains low computational cost.

Abstract

Hashing has emerged as a popular technique for large-scale similarity search. Most learning-based hashing methods generate compact yet correlated hash codes. However, this redundancy is storage-inefficient. Hence we propose a lossless variable-length hashing (VLH) method that is both storage- and search-efficient. Storage efficiency is achieved by converting the fixed-length hash code into a variable-length code. Search efficiency is obtained by using a multiple hash table structure. With VLH, we are able to deliberately add redundancy into hash codes to improve retrieval performance with little sacrifice in storage efficiency or search complexity. In particular, we propose a block K-means hashing (B-KMH) method to obtain significantly improved retrieval performance with no increase in storage and marginal increase in computational cost.