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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 BFS-Tree of ranking references for unsupervised manifold learning

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Author(s):
Guimaraes Pedronette, Daniel Carlos [1] ; Valem, Lucas Pascotti [1] ; Torres, Ricardo da S. [2]
Total Authors: 3
Affiliation:
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro - Brazil
[2] NTNU Norwegian Univ Sci & Technol, Fac Informat Technol & Elect Engn, Dept ICT & Nat Sci, Alesund - Norway
Total Affiliations: 2
Document type: Journal article
Source: PATTERN RECOGNITION; v. 111, MAR 2021.
Web of Science Citations: 0
Abstract

Contextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper, a novel unsupervised manifold learning is proposed through a similarity representation based on ranking references. A breadth-first tree is used to represent similarity information given by ranking references and is exploited to discovery underlying similarity relationships. As a result, a more effective similarity measure is computed, which leads to more relevant objects in the returned ranked lists of search sessions. Several experiments conducted on eight public datasets, commonly used for image retrieval benchmarking, demonstrated that the proposed method achieves very high effectiveness results, which are comparable or superior to the ones produced by state-of-the-art approaches. (C) 2020 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 16/50250-1 - The secret of playing football: Brazil versus the Netherlands
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Support type: Research Projects - Thematic Grants
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Support type: Research Projects - Thematic Grants
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Support type: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)
FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants - Phase 2
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
FAPESP's process: 17/20945-0 - Multi-user equipment approved in great 16/50250-1: local positioning system
Grantee:Sergio Augusto Cunha
Support type: Multi-user Equipment Program
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support type: Research Projects - Thematic Grants
FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
Grantee:João Paulo Papa
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
Grantee:Rubens Augusto Camargo Lamparelli
Support type: Research Grants - Research Partnership for Technological Innovation - PITE