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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.)

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

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Guimaraes Pedronette, Daniel Carlos [1] ; Fernandes Goncalves, Filipe Marcel [1] ; Guilherme, Ivan Rizzo [1]
Total Authors: 3
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro - Brazil
Total Affiliations: 1
Document type: Journal article
Source: PATTERN RECOGNITION; v. 75, n. SI, p. 161-174, MAR 2018.
Web of Science Citations: 12

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)

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