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Aplication and investigation of unsupervised learning methods in retrieval and classification tasks

Grant number: 18/15597-6
Support type:Research Grants - Young Investigators Grants- Phase 2
Duration: May 01, 2019 - April 30, 2024
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Daniel Carlos Guimarães Pedronette
Home Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Assoc. researchers:Denis Henrique Pinheiro Salvadeo ; Fabricio Aparecido Breve ; João Paulo Papa ; Jurandy Gomes de Almeida Junior ; Ricardo da Silva Torres
Associated research grant:13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks, AP.JP
Associated scholarship(s):20/02183-9 - Rank-based unsupervised learning through deep learning in diverse domains, BP.PD
20/03311-0 - Unsupervised learning for general and multimodal multimedia retrieval, BP.MS

Abstract

Unsupervised Learning methods have been established as a solution to increase the effectiveness of content-based searches without requiring user intervention. These methods exploit contextual relationship among images, usually encoded in the distance/similarity information of the collections.This research project intends to investigate the application of such methods in new and diversified domains. Unsupervised learning methods reevaluate the similarity between the elements of the collection and can be used as a pre-processing step in classification tasks. In addition, initial results indicate that the methods can be applied in general multimedia and multimodal retrieval scenarios, considering audio and video.Therefore, the central objective of the proposed project is to deepen such investigation, expanding the domains of application of unsupervised learning. methods. (AU)

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS. Graph -based selective rank fusion for unsupervised image retrieval. PATTERN RECOGNITION LETTERS, v. 135, p. 82-89, JUL 2020. Web of Science Citations: 0.
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS. Unsupervised selective rank fusion for image retrieval tasks. Neurocomputing, v. 377, p. 182-199, FEB 15 2020. Web of Science Citations: 0.
GUIMARAES PEDRONETTE, DANIEL CARLOS; VALEM, LUCAS PASCOTTI; ALMEIDA, JURANDY; TONES, RICARDO DA S. Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking. IEEE Transactions on Image Processing, v. 28, n. 12, p. 5824-5838, DEC 2019. Web of Science Citations: 0.
CAMPOS, VICTOR DE ABREU; GUIMARAES PEDRONETTE, DANIEL CARLOS. A framework for speaker retrieval and identification through unsupervised learning. COMPUTER SPEECH AND LANGUAGE, v. 58, p. 153-174, NOV 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.