Proximity Graphs for Similarity Searches: Experimental Survey and the New Connected-Partition Approach HGraph

  • Larissa C. Shimomura Universidade de Londrina (UEL)
  • Daniel S. Kaster Universidade de Londrina (UEL)

Resumo


Similarity searching is a widely used approach to retrieve complex data (images, videos, time series, etc.). Similarity searches aim at retrieving similar data according to the intrinsic characteristics of the data. Recently, graph-based methods have emerged as a very efficient alternative for similarity retrieval, with reports indicating they have outperformed methods of other categories in several situations. This work presents two main contributions to graph-based methods for similarity searches. The first contribution is a survey on the main graph types currently employed for similarity searches and an experimental evaluation of the most representative graphs in a common platform for exact and approximate search algorithms. The second contribution is a new graph-based method called HGraph, which is a connected-partition approach to build a proximity graph and answer similarity searches. Both of our contributions and results were published and received awards in international conferences.

Palavras-chave: similarity search, graph, HGraph

Referências

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Publicado
04/10/2021
Como Citar

Selecione um Formato
SHIMOMURA, Larissa C.; KASTER, Daniel S.. Proximity Graphs for Similarity Searches: Experimental Survey and the New Connected-Partition Approach HGraph. In: CONCURSO DE TESES E DISSERTAÇÕES (CTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 171-176. DOI: https://doi.org/10.5753/sbbd_estendido.2021.18181.