用沃罗诺伊图构建无需训练的哈希函数，速度快效果好。
Voronoi Diagram Encoded Hashing
- 基于数据相似性构造沃罗诺伊图生成哈希函数
- 相同位长下准确率超越现有最优方法，计算成本更低
- 适合追求高效低开销哈希的工程应用
学习哈希（L2H）旨在从数据分布中学习数据依赖的哈希函数，将输入空间的数据映射到二进制编码空间。尽管L2H取得成功，但两项观察对其有效性提出质疑：其一，无学习的局部敏感哈希在相同精度下耗时更少；其二，现有L2H方法仅限于阈值、超球面和超平面三类哈希函数。本文揭示沃罗诺伊图在哈希中的潜力，因其具备三项关键属性。据此提出一种无需学习的简单高效二值哈希方法——沃罗诺伊图编码哈希（VDeH），通过数据依赖的相似性度量构建哈希函数集，并以编码哈希生成独立二进制位。实验表明，在多个基准数据集上，VDeH在相同位长下性能优于现有最先进方法，且计算成本更低。
原文摘要 · Abstract (English)
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.
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