HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search

TL;DR

HDIdx is an open-source Python library that enables fast approximate nearest neighbor search in high-dimensional data by converting vectors into compact binary codes, improving efficiency and scalability.

Contribution

The paper introduces HDIdx, a novel high-dimensional indexing library that employs state-of-the-art algorithms for efficient approximate NN search using binary codes.

Findings

Offers low space complexity for high-dimensional data

Achieves fast search times with approximate methods

Scalable to large datasets

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.