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(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.)

A graph-based ranked-list model for unsupervised distance learning on shape retrieval

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Autor(es):
Guimaraes Pedronette, Daniel Carlos ; Almeida, Jurandy ; Torres, Ricardo da S.
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: PATTERN RECOGNITION LETTERS; v. 83, n. 3, p. 357-367, NOV 1 2016.
Citações Web of Science: 5
Resumo

Several re-ranking algorithms have been proposed recently. Some effective approaches are based on complex graph-based diffusion processes, which usually are time consuming and therefore inappropriate for real-world large scale shape collections. In this paper, we introduce a novel graph-based approach for iterative distance learning in shape retrieval tasks. The proposed method is based on the combination of graphs defined in terms of multiple ranked lists. The efficiency of the method is guaranteed by the use of only top positions of ranked lists in the definition of graphs that encode reciprocal references. Effectiveness analysis performed in three widely used shape datasets demonstrate that the proposed graph-based ranked-list model yields significant gains (up to +55.52%) when compared with the use of shape descriptors in isolation. Furthermore, the proposed method also yields comparable or superior effectiveness scores when compared with several state-of-the-art approaches. (C) 2016 Elsevier B.V. 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
Processo FAPESP: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Beneficiário:Ricardo da Silva Torres
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE