Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Unsupervised manifold learning through reciprocal kNN graph and Connected Components for image retrieval tasks

Texto completo
Autor(es):
Guimaraes Pedronette, Daniel Carlos [1] ; Fernandes Goncalves, Filipe Marcel [1] ; Guilherme, Ivan Rizzo [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: PATTERN RECOGNITION; v. 75, n. SI, p. 161-174, MAR 2018.
Citações Web of Science: 12
Resumo

Performing effective image retrieval tasks, capable of exploiting the underlying structure of datasets still constitutes a challenge research scenario. This paper proposes a novel manifold learning approach that exploits the intrinsic dataset geometry for improving the effectiveness of image retrieval tasks. The underlying dataset manifold is modeled and analyzed in terms of a Reciprocal kNN Graph and its Connected Components. The method computes the new retrieval results on an unsupervised way, without the need of any user intervention. A large experimental evaluation was conducted, considering different image retrieval tasks, various datasets and features. The proposed method yields better effectiveness results than various methods recently proposed, achieving effectiveness gains up to +40.75%. (C) 2017 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 13/08645-0 - Reclassificação e agregação de listas para tarefas de recuperação de imagens
Beneficiário:Daniel Carlos Guimarães Pedronette
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores