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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 Rank Correlation and kNN Sets

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Author(s):
Valem, Lucas Pascotti [1] ; De Oliveira, Carlos Renan [1] ; Guimaraes Pedronette, Daniel Carlos [1] ; Almeida, Jurandy [2]
Total Authors: 4
Affiliation:
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp, Av 24-A, 1515, BR-13506900 Rio Claro, SP - Brazil
[2] Univ Fed Sao Paulo UNIFESP, Inst Ciencia & Tecnol, Av Cesare MG Lattes 1201, BR-12247014 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: ACM Transactions on Multimedia Computing Communications and Applications; v. 14, n. 4 NOV 2018.
Web of Science Citations: 1
Abstract

The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different k-Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top-k lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison with previous works. In addition, we also evaluate the ability of the method in considering different multimedia objects, conducting an extensive experimental evaluation on various image and video datasets. (AU)

FAPESP's process: 16/06441-7 - Semantic information retrieval in large video databases
Grantee:Jurandy Gomes de Almeida Junior
Support type: Regular Research 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: 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: 17/02091-4 - Selection and combination of unsupervised learning Methdos for content-based image retrieval
Grantee:Lucas Pascotti Valem
Support type: Scholarships in Brazil - Master