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FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION

2009· article· en· 2,602 citations· W1627400044 on OpenAlex· 10.5220/0001787803310340

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Abstract

nearest-neighbors search, randomized kd-trees, hierarchical k-means tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection. 1

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The record

Venue
Topic
Advanced Image and Video Retrieval Techniques
Field
Computer Science
Canadian institutions
University of British Columbia
Funders
Natural Sciences and Engineering Research Council of Canada
Keywords
Computer sciencek-nearest neighbors algorithmAlgorithmNearest-neighbor chain algorithmArtificial intelligenceCluster analysis
Has abstract in OpenAlex
yes