Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey
Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey
复制标题
用于大数据近似最近邻搜索的二进制哈希：一项调查
DOI：
10.1109/access.2017.2781360
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Gui, Jie
中科院分区：
文献类型：
--
作者：
Cao, Yuan;Qi, Heng;Gui, Jie
最近邻搜索是计算机视觉、数据挖掘和机器学习等领域的一个基本问题。随着互联网上数据的爆炸式增长，许多使用空间划分和递归超平面分解的新数据结构（例如，k-d树）来加速最近邻搜索。然而，这些数据结构正面临着大数据的挑战。为了应对这些挑战，基于二进制哈希的近似最近邻搜索方法由于其快速的查询速度和大大减少的存储而引起了人们的极大关注。自从最著名的局部敏感哈希算法提出以来，出现了大量的二进制哈希算法。在本文中，我们首先说明了二进制哈希研究的发展，提出了一个全面和明确的分类。然后，我们进行了广泛的实验，比较这些方法在五个著名的和公开的数据集的性能。最后，我们提出了我们对这个问题的看法。
Nearest neighbor search is a fundamental problem in various domains, such as computer vision, data mining, and machine learning. With the explosive growth of data on the Internet, many new data structures using spatial partitions and recursive hyperplane decomposition (e.g., k-d trees) are proposed to speed up the nearest neighbor search. However, these data structures are facing big data challenges. To meet these challenges, binary hashing-based approximate nearest neighbor search methods attract substantial attention due to their fast query speed and drastically reduced storage. Since the most notably locality sensitive hashing was proposed, a large number of binary hashing methods have emerged. In this paper, we first illustrate the development of binary hashing research by proposing an overall and clear classification of them. Then we conduct extensive experiments to compare the performance of these methods on five famous and public data sets. Finally, we present our view on this topic.