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.
High-dimensional indexing, Approximate Nearest Neighbor Search, Product Quantization, Spectral Hashing
Computer Sciences | Databases and Information Systems
Data Management and Analytics
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. 1-5. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3413
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.