HDIdx: High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search
This work provides a practical tool for multimedia analytics, but it is incremental as it builds on existing state-of-the-art algorithms.
The paper tackles the problem of fast nearest neighbor search in high-dimensional data by introducing HDIdx, an open-source Python library that uses binary coding to achieve efficient and scalable approximate search with low space complexity.
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.