Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/339482
Type: Artigo
Title: Hierarchical clustering-based graphs for large scale approximate nearest neighbor search
Author: Munoz, Javier Vargas
Goncalves, Marcos A.
Dias, Zanoni
Torres, Ricardo da S.
Abstract: This paper presents a novel approach to perform fast approximate nearest neighbors search in high dimensional data, using a nearest neighbor graph created over large collections. This graph is created based on the fusion of multiple hierarchical clustering results, where a minimum-spanning-tree structure is used to connect all elements in a cluster. We propose a novel search technique to guide the navigation on the graph without computing exhaustively the distances to all neighbors in each step of the search, just to those in the direction of the query. The objective is to determine the nearest point to the query with a few number of distance calculations. We experimented in three datasets of 1 million SIFT, GIST, and GloVe features. Results show better speedups than another graph-based technique, and competitive speedups at high recall values when compared to classic and recent state-of-the-art techniques
Subject: Cluster
Country: Países Baixos
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.patcog.2019.106970
Address: https://www.sciencedirect.com/science/article/pii/S0031320319302730
Date Issue: 2019
Appears in Collections:IC - Artigos e Outros Documentos

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