Nearest neighbor search is a problem of finding the data points from the feature space such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, and categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.

本文系统综述了最近邻搜索问题中的哈希学习算法，将其按照不同的相似性保存方式进行分类，并分别阐述其性能评估和效益分析，最终指出量化算法在搜索精度、搜索时间、空间花费等方面都表现优异，并介绍了一些新兴话题。

学习哈希的调查