Exploring contextual classification approaches for Optimum-Path Forest

• Grant number: 12/06472-9
• Support type: Scholarships in Brazil - Doctorate
• Effective date (Start): July 01, 2012
• Effective date (End): June 30, 2016
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: João Paulo Papa
• Grantee: Daniel Osaku
• Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated research grant: 09/16206-1 - New trends on optimum-path forest-based pattern recognition, AP.JP

Abstract

Pattern recognition techniques have attracted much attention in the last years, in which data samples are feature-based representations of images, signals and videos. However, the assumption of statistically independent data may not be handled in some situations. Therefore, the study of techniques that employ contextual information has grown in the scientific community, since it may increase the recognition rates. In this project, we propose to consider the contextual information for Optimum-Path Forest classifier, since it has neven been fone before. We also intend to stablish a relation between Markov Random Fields and Information Theory in this research, since the former has been extensively used for contextual-based classification, and the latter can also be employed for the same task.

Scientific publications

(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)

OSAKU, DANIEL; PEREIRA, DANILLO R.; LEVADA, ALEXANDRE L. M.; PAPA, JOAO P. Fine-Tuning Contextual-Based Optimum-Path Forest for Land-Cover Classification. IEEE Geoscience and Remote Sensing Letters, v. 13, n. 5, p. 735-739, MAY 2016. Web of Science Citations: 1.

PEREIRA, DANILLO R.; PAZOTI, MARIO A.; PEREIRA, LUIS A. M.; RODRIGUES, DOUGLAS; RAMOS, CAIO O.; SOUZA, ANDRE N.; PAPA, JOAO P. Social-Spider Optimization-based Support Vector Machines applied for energy theft detection. COMPUTERS & ELECTRICAL ENGINEERING, v. 49, p. 25-38, JAN 2016. Web of Science Citations: 17.

OSAKU, D.; NAKAMURA, R. Y. M.; PEREIRA, L. A. M.; PISANI, R. J.; LEVADA, A. L. M.; CAPPABIANCO, F. A. M.; FALCO, A. X.; PAPA, JOAO P. Improving land cover classification through contextual-based optimum-path forest. INFORMATION SCIENCES, v. 324, p. 60-87, DEC 10 2015. Web of Science Citations: 13.