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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Guimaraes Pedronette, Daniel Carlos ; Almeida, Jurandy ; Torres, Ricardo da S.
Total Authors: 3
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 83, n. 3, p. 357-367, NOV 1 2016.
Web of Science Citations: 5
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. (AU)

FAPESP's process: 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Grantee:Ricardo da Silva Torres
Support type: Research Grants - Research Partnership for Technological Innovation - PITE