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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 similarity learning through Cartesian product of ranking references

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Valem, Lucas Pascotti [1] ; Guimaraes Pedronette, Daniel Carlos [1] ; Almeida, Jurandy [2]
Total Authors: 3
[1] State Univ Sao Paulo UNESP, Dept Stat Appl Math & Comp, Av 24-A, 1515, BR-13506900 Rio Claro, SP - Brazil
[2] Fed Univ Sao Paulo UNIFESP, Inst Sci & Technol, Av Cesare MG Lattes 1201, BR-12247014 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 114, n. SI, p. 41-52, OCT 15 2018.
Web of Science Citations: 2

Despite the consistent advances in visual features and other Multimedia Information Retrieval (MIR) techniques, measuring the similarity among multimedia objects is still a challenging task for an effective retrieval. In this scenario, similarity learning approaches capable of improving the effectiveness of retrieval in an unsupervised way are indispensable. A novel method, called Cartesian Product of Ranking References (CPRR), is proposed with this objective in this paper. The proposed method uses Cartesian product operations based on rank information for exploiting the underlying structure of datasets. Only subsets of ranked lists are required, demanding low computational efforts. An extensive experimental evaluation was conducted considering various aspects, seven public multimedia datasets (images and videos) and several different features. Besides effectiveness, experiments were also conducted to assess the efficiency of the method, considering parallel and heterogeneous computing on CPU and GPU devices. The proposed method achieved significant effectiveness gains, including competitive state-of-the-art results on popular benchmarks. Keywords: Content-based image retrieval Unsupervised learning Cartesian product Effectiveness Efficiency (C) 2017 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/04220-8 - Lists efficient re-ranking and rank aggregation methods
Grantee:Lucas Pascotti Valem
Support Opportunities: Scholarships in Brazil - Scientific Initiation
FAPESP's process: 17/02091-4 - Selection and combination of unsupervised learning Methdos for content-based image retrieval
Grantee:Lucas Pascotti Valem
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 16/06441-7 - Semantic information retrieval in large video databases
Grantee:Jurandy Gomes de Almeida Junior
Support Opportunities: Regular Research Grants
FAPESP's process: 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support Opportunities: Research Grants - Young Investigators Grants