The basic idea of HDIdx is to compress the original feature vectors into compact binary codes, and perform approximate NN search instead of extract NN search. This can largely reduce the storage requirements and can significantly speed up the search.
Oct 7, 2015 · In this work, we present "HDIdx", an efficient high-dimensional indexing library for fast approximate NN search, which is open-source and ...
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 ...
An efficient high-dimensional indexing library called HDIdx was introduced in [10] for estimated NN search. It transformed the input high-dimensional vectors ...
May 10, 2017 · This makes NN search on large-scale high-dimensional data very challenging given limited storage and computational resources. Instead of ...
This article addresses the fundamental problem of learning to optimize image similarity with sparse and high-dimensional representations from large-scale ...
People also ask
What is the approximate nearest neighbor index?
What are the methods for approximate nearest neighbor search?
What is the nearest neighbor algorithm approximation?
How does Annoy work?
Apr 22, 2011 · I currently study such problems -- classification, nearest neighbor searching -- for music information retrieval. You may be interested in ...
Missing: HDIdx: | Show results with:HDIdx:
High-Dimensional Indexing for Efficient Approximate Nearest Neighbor Search - hdidx.
Aug 22, 2015 · Precision: The nearest neighbors must be found (not approximations). Speed: The search must be as fast as possible. (The time to create the data ...
Missing: HDIdx: indexing
Nov 18, 2023 · To select a library for approximate nearest neighbor search, several factors should be considered. Firstly, the accuracy of the matching ...