EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph
Cong Fu, Deng Cai

TL;DR
EFANNA is a novel algorithm that significantly accelerates approximate nearest neighbor search and graph construction by combining hierarchical methods with kNN graph initialization, outperforming existing algorithms in speed.
Contribution
The paper introduces EFANNA, the fastest algorithm for approximate nearest neighbor search and graph construction, leveraging a good initialization to overcome local optima and reduce construction time.
Findings
EFANNA outperforms state-of-the-art algorithms in speed.
EFANNA effectively combines hierarchical and graph-based methods.
EFANNA achieves the fastest approximate nearest neighbor search and graph construction.
Abstract
Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision. The performance of traditional hierarchical structure (tree) based methods decreases as the dimensionality of data grows, while hashing based methods usually lack efficiency in practice. Recently, the graph based methods have drawn considerable attention. The main idea is that \emph{a neighbor of a neighbor is also likely to be a neighbor}, which we refer as \emph{NN-expansion}. These methods construct a -nearest neighbor (NN) graph offline. And at online search stage, these methods find candidate neighbors of a query point in some way (\eg, random selection), and then check the neighbors of these candidate neighbors for closer ones iteratively. Despite some promising results, there are mainly two problems with these approaches: 1) These approaches…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods · Data Management and Algorithms
