Rank-based unsupervised learning through deep learning in diverse domains

• Grant number: 20/02183-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: April 01, 2020
• End date: February 29, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: Vanessa Helena Pereira Ferrero
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
• Associated research grant: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2

Abstract

Ranking-based 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 relationships among images, usually encoded in the distance/similarity information of the collections. Concomitantly, strategies based on deep learning have assumed a prominent position in the most diverse machine learning tasks. Thus, this research project aims to combine these two lines of research, investigating new unsupervised learning methods that are based on ranking information and deep learning. In addition, it is also intended to investigate other retrieval and classification scenarios, expanding the domains of application of unsupervised learning methods. (AU)

Scientific publications

(6)

(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)

DE ALMEIDA, LUCAS BARBOSA; PEREIRA-FERRERO, VANESSA HELENA; VALEM, LUCAS PASCOTTI; ALMEIDA, JURANDY; GUIMARAES PEDRONETTE, DANIEL CARLOS; IEEE COMP SOC. Representation Learning for Image Retrieval through 3D CNN and Manifold Ranking. 2021 34TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2021), v. N/A, p. 8-pg., 2021-01-01. (18/15597-6, 20/02183-9, 17/25908-6)

VALEM, LUCAS PASCOTTI; SATO KAWAI, VINICIUS ATSUSHI; PEREIRA-FERRERO, VANESSA HELENA; GUIMARAES PEDRONETTE, DANIEL CARLOS; IEEE. A NOVEL RANK CORRELATION MEASURE FOR MANIFOLD LEARNING ON IMAGE RETRIEVAL AND PERSON RE-ID. 2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, v. N/A, p. 5-pg., 2022-01-01. (18/15597-6, 20/02183-9, 21/07993-1, 20/11366-0)

PEREIRA-FERRERO, VANESSA HELENA; VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL CARLOS GUIMARAES. Feature augmentation based on manifold ranking and LSTM for image classification (R). EXPERT SYSTEMS WITH APPLICATIONS, v. 213, p. 16-pg., 2023-03-01. (17/25908-6, 20/02183-9, 18/15597-6, 20/11366-0)

PEREIRA-FERRERO, V. H.; LEWIS, T. G.; VALEM, L. P.; FERRERO, L. G. P.; PEDRONETTE, D. C. G.; LATECKI, L. J.. Unsupervised affinity learning based on manifold analysis for image retrieval: A survey. COMPUTER SCIENCE REVIEW, v. 53, p. 23-pg., 2024-07-29. (20/02183-9, 18/15597-6)

ROZIN, BIONDA; PEREIRA-FERRERO, VANESSA HELENA; LOPES, LEONARDO TADEU; PEDRONETTE, DANIEL CARLOS GUIMARAES. A rank-based framework through manifold learning for improved clustering tasks. INFORMATION SCIENCES, v. 580, p. 202-220, NOV 2021. (17/25908-6, 20/02183-9, 20/08854-2, 18/15597-6)

PEREIRA-FERRERO, VANESSA HELENA; VALEM, LUCAS PASCOTTI; LETICIO, GUSTAVO ROSSETO; PEDRONETTE, DANIEL CARLOS GUIMARAES. Feature Fusion and Augmentation Based on Manifold Ranking for Image Classification. INTERNATIONAL JOURNAL OF SEMANTIC COMPUTING, v. 18, n. 04, p. 22-pg., 2024-08-16. (20/02183-9, 18/15597-6, 17/25908-6)