HDIdx: High-dimensional indexing for efficient approximate nearest neighbor search

HDIdx: High-dimensional indexing for efficient approximate nearest neighbor search

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 a...

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Bibliographic Details
Main Authors: WAN, Ji, TANG, Sheng, ZHANG, Yongdong, LI, Jintao, WU, Pengcheng, HOI, Steven C. H.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
High-dimensional indexing
Approximate Nearest Neighbor Search
Product Quantization
Spectral Hashing
Computer Sciences
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/3413
https://ink.library.smu.edu.sg/context/sis_research/article/4414/viewcontent/HDIdx_Neurocomputing_2016_pp.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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