期刊
IEEE ACCESS
卷 6, 期 -, 页码 2039-2054出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2781360
关键词
Approximate nearest neighbor search; large-scale database; hashing based methods; overview
资金
- NSFC [61772112, 61672379, 61370199, 61572463]
- State Key Program of National Natural Science of China [61432002]
- Dalian High-level Talent Innovation Program [2015R049]
- JSPS KAKENHI [16F16349]
- China Scholarship Council
- Grants-in-Aid for Scientific Research [16F16349] Funding Source: KAKEN
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.
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