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Selection and combination of unsupervised learning Methdos for content-based image retrieval

Grant number: 17/02091-4
Support type:Scholarships in Brazil - Master
Effective date (Start): May 01, 2017
Effective date (End): March 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Lucas Pascotti Valem
Home Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Associated research grant:13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks, AP.JP

Abstract

The evolution of the mobile devices and the consequent facilities in the processes of acquisition and sharing of visual content have caused a great growth in the collections of images. In this scenario, it becomes imperative to develop automatic methods that allow indexing, analyzing and searching in such content. Content-Based Image Retrieval (CBIR) are a promising solution. However, assessing the similarity between the visual content of images presents several challenges. Unsupervised Learning have firmed as a solution to improve the effectiveness of retrieval systems without requiring user intervention. As several methods have been developed using distinct and complementary approaches, new research challenges emerge regarding the combination of these methods in order to increase the gains. This research project intends to investigate and propose strategies for selection and aggregation the unsupervised learning methods. (AU)

Scientific publications (5)
(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.
VALEM, LUCAS PASCOTTI; DE OLIVEIRA, CARLOS RENAN; GUIMARAES PEDRONETTE, DANIEL CARLOS; ALMEIDA, JURANDY. Unsupervised Similarity Learning through Rank Correlation and kNN Sets. ACM Transactions on Multimedia Computing Communications and Applications, v. 14, n. 4 NOV 2018. Web of Science Citations: 1.
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ALMEIDA, JURANDY. Unsupervised similarity learning through Cartesian product of ranking references. PATTERN RECOGNITION LETTERS, v. 114, n. SI, p. 41-52, OCT 15 2018. Web of Science Citations: 2.
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
VALEM, Lucas Pascotti. Combinação seletiva não supervisionada de listas ranqueadas aplicada à busca de imagens pelo conteúdo. 2019. 106 f. Master's Dissertation - Universidade Estadual Paulista "Júlio de Mesquita Filho" Instituto de Biociências, Letras e Ciências Exatas..

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