Publication Type
Journal Article
Version
submittedVersion
Publication Date
5-2016
Abstract
Fast Nearest Neighbor (NN) search is a fundamental challenge in large-scale data processing and analytics, particularly for analyzing multimedia contents which are often of high dimensionality. Instead of using exact NN search, extensive research efforts have been focusing on approximate NN search algorithms. In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and written in Python. It offers a family of state-of-the-art algorithms that convert input high-dimensional vectors into compact binary codes, making them very efficient and scalable for NN search with very low space complexity.
Keywords
High-dimensional indexing, Approximate Nearest Neighbor Search, Product Quantization, Spectral Hashing
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Neurocomputing
Volume
237
First Page
401
Last Page
404
ISSN
0925-2312
Identifier
10.1016/j.neucom.2015.11.104
Publisher
Elsevier
Citation
WAN, Ji; TANG, Sheng; ZHANG, Yongdong; LI, Jintao; WU, Pengcheng; and HOI, Steven C. H..
HDIdx: High-dimensional indexing for efficient approximate nearest neighbor search. (2016). Neurocomputing. 237, 401-404.
Available at: https://ink.library.smu.edu.sg/sis_research/3413
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.neucom.2015.11.104