Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/329524
Type: Artigo
Title: A Graph-based Ranked-list Model For Unsupervised Distance Learning On Shape Retrieval
A graph-based ranked-list model for unsupervised distance learning on shape retrieval
Author: Pedronette, Daniel Carlos Guimarães
Almeida, Jurandy
Torres, Ricardo da S.
Abstract: 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.
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 p
Subject: Ensino a distância
Re-ranqueamento
Country: Países Baixos
Editor: Elsevier
Citation: Pattern Recognition Letters. Elsevier Science Bv, v. 83, p. 357 - 367, 2016.
Rights: fechado
Fechado
Identifier DOI: 10.1016/j.patrec.2016.05.021
Address: https://www.sciencedirect.com/science/article/pii/S0167865516301052
Date Issue: 2016
Appears in Collections:IC - Artigos e Outros Documentos
FT - Artigos e Outros Documentos

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