Voronoi Diagram Encoded Hashing

TL;DR

This paper introduces VDeH, a novel no-learning hashing method using Voronoi diagrams, which achieves superior accuracy and efficiency compared to traditional learning-based approaches.

Contribution

The paper proposes a simple, data-dependent Voronoi diagram-based hashing method that does not require learning, expanding the types of hash functions used in L2H.

Findings

VDeH outperforms state-of-the-art methods in accuracy.

VDeH has lower computational cost.

VDeH achieves superior performance on benchmark datasets.

Abstract

The goal of learning to hash (L2H) is to derive data-dependent hash functions from a given data distribution in order to map data from the input space to a binary coding space. Despite the success of L2H, two observations have cast doubt on the source of the power of L2H, i.e., learning. First, a recent study shows that even using a version of locality sensitive hashing functions without learning achieves binary representations that have comparable accuracy as those of L2H, but with less time cost. Second, existing L2H methods are constrained to three types of hash functions: thresholding, hyperspheres, and hyperplanes only. In this paper, we unveil the potential of Voronoi diagrams in hashing. Voronoi diagram is a suitable candidate because of its three properties. This discovery has led us to propose a simple and efficient no-learning binary hashing method, called Voronoi Diagram Encoded Hashing (VDeH), which constructs a set of hash functions through a data-dependent similarity measure and produces independent binary bits through encoded hashing. We demonstrate through experiments on several benchmark datasets that VDeH achieves superior performance and lower computational cost compared to existing state-of-the-art methods under the same bit length.