Complex networks and content-based image retrieval supported by selective visual attention features

• Grant number: 20/10049-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: September 01, 2020
• End date: October 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Agma Juci Machado Traina
• Grantee: Cláudio Douglas Gouveia Linhares
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD), AP.TEM

Abstract

The goal of this project is to explore the use of selective visual attention models to identify the most important visual regions in an image and use this information to build feature extraction techniques that allow mitigating the semantic gap between human perception and computational description. These models can lead attention to specific places of interest in the image according to human perception and/or the application domain. The motivation is to enrich the construction of computer systems closer to the human interpretation, in order to improve the solution of difficult problems for many applications, such as object recognition through complex networks and content-based image retrieval. Thus, it complex networks will be used as a way of identifying the relationship between the images and their structure, in order to boost their usability from different metrics and pre-existing visualization techniques, paying attention to scalability issues. The final objective of this project is to develop a theoretical and practical tool that can characterize the visual content of the images in compact, scalable and discriminative data, seeking to obtain a content-based image retrieval system that has effective and efficient perceptual performance. (AU)

Scientific publications

(9)

(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)

PONCIANO, JEAN R.; LINHARES, CLAUDIO D. G.; FARIA, ELAINE R.; TRAVENCOLO, BRUNO A. N.. An online and nonuniform timeslicing method for network visualisation & nbsp;. COMPUTERS & GRAPHICS-UK, v. 97, p. 170-182, JUN 2021. (20/10049-0)

LINHARES, CLAUDIO D. G.; PONCIANO, JEAN R.; PAIVA, JOSE GUSTAVO S.; TRAVENCOLO, BRUNO A. N.; ROCHA, LUIS E. C.. A comparative analysis for visualizing the temporal evolution of contact networks: a user study. JOURNAL OF VISUALIZATION, JUN 2021. (20/10049-0, 16/17078-0)

ROCHA, LUIS E. C.; HOLME, PETTER; LINHARES, CLAUDIO D. G.. The global migration network of sex-workers. JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, v. 5, n. 1, p. 17-pg., 2022-01-13. (16/17078-0, 20/10049-0)

LINHARES, CLAUDIO D. G.; PONCIANO, JEAN R.; PAIVA, JOSE GUSTAVO S.; TRAVENCOLO, BRUNO A. N.; ROCHA, LUIS E. C.. A comparative analysis for visualizing the temporal evolution of contact networks: a user study. JOURNAL OF VISUALIZATION, v. 24, n. 5, p. 1011-1031, OCT 2021. (16/17078-0, 20/10049-0)

PONCIANO, JEAN R.; LINHARES, CLAUDIO D. G.; ROCHA, LUIS E. C.; FARIA, ELAINE R.; TRAVENCOLO, BRUNO A. N.; ACM. Combining clutter reduction methods for temporal network visualization. 37TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, v. N/A, p. 8-pg., 2022-01-01. (20/10049-0, 16/17078-0, 20/07200-9)

LINHARES, CLAUDIO D. G.; LIMA, DANIEL M.; PONCIANO, JEAN R.; OLIVATTO, MAURO M.; GUTIERREZ, MARCO A.; POCO, JORGE; TRAINA JR, CAETANO; TRAINA, AGMA J. M.. ClinicalPath: A Visualization Tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v. 29, n. 10, p. 16-pg., 2023-10-01. (20/10049-0, 16/17078-0, 20/07200-9)

LINHARES, CLAUDIO D. G.; TINOCO, CLAUDINEY R.; PONCIANO, JEAN R.; OLIVEIRA, GINA M. B.; TRAVENCOLO, BRUNO A. N.; XAVIER-JUNIOR, JC; RIOS, RA. A Network-Based Visual Analytics Approach for Performance Evaluation of Swarms of Robots in the Surveillance Task. INTELLIGENT SYSTEMS, PT I, v. 13653, p. 16-pg., 2022-01-01. (20/07200-9, 16/17078-0, 20/10049-0)

LINHARES, CLAUDIO D. G.; LIMA, DANIEL M.; BONES, CHRISTIAN C.; REBELO, MARINA F. S.; GUTIERREZ, MARCO A.; TRAINA JR, CAETANO; TRAINA, AGMA J. M.; ALMEIDA, JR; GONZALEZ, AR; SHEN, L; et al. I-CovidVis - A Visual Analytics Tool for Interoperable Healthcare Databases using Graphs. 2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), v. N/A, p. 6-pg., 2021-01-01. (20/07200-9, 16/17078-0, 19/04660-1, 20/10049-0)

PONCIANO, JEAN R.; LINHARES, CLAUDIO D. G.; FARIA, ELAINE R.; TRAVENCOLO, BRUNO A. N.. An online and nonuniform timeslicing method for network visualisation. COMPUTERS & GRAPHICS-UK, v. 97, p. 13-pg., 2021-05-11. (20/10049-0)