Order preserving hashing for approximate nearest neighbor search

Jianfeng Wang, Jingdong Wang, Nenghai Yu, Shipeng Li
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引用次数: 111

Abstract

In this paper, we propose a novel method to learn similarity-preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the original space and the ones in the hamming space. The problem of mapping the NN points into different hash codes is taken as a classification problem in which the points are categorized into several groups according to the hamming distances to the query. The hash functions are optimized from the classifiers pooled over the training points. Experimental results demonstrate the superiority of our approach over existing state-of-the-art hashing techniques.
近似最近邻搜索的保序哈希
在本文中,我们提出了一种学习近似最近邻(NN)搜索中保持相似哈希函数的新方法。关键思想是通过最大化从原始空间计算的相似顺序与汉明空间中的相似顺序之间的对齐来学习哈希函数。将神经网络点映射到不同哈希码的问题作为分类问题,根据到查询的汉明距离将点分成几组。哈希函数通过在训练点上池化的分类器进行优化。实验结果表明,我们的方法优于现有的最先进的哈希技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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