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 Guimaraes Pedronette, Daniel Carlos Google Scholar
Almeida, Jurandy Autor UNIFESP Google Scholar
Torres, Ricardo da S. Google Scholar
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.
Keywords Shape retrieval
Ranking methods
Graph-based approaches
xmlui.dri2xhtml.METS-1.0.item-coverage Amsterdam
Language English
Sponsor Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Microsoft Research
Grant number FAPESP: 2013/08645-0
FAPESP: 2013/50169-1
CNPq: 306580/2012-8
CNPq: 484254/2012-0
Date 2016
Published in Pattern Recognition Letters. Amsterdam, v. 83, p. 357-367, 2016.
ISSN 0167-8655 (Sherpa/Romeo, impact factor)
Publisher Elsevier Science Bv
Extent 357-367
Origin http://dx.doi.org/10.1016/j.patrec.2016.05.021
Access rights Closed access
Type Article
Web of Science ID WOS:000386874900015
URI https://repositorio.unifesp.br/handle/11600/56852

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