Fine-tuning contextual-based optimum path forest for land cover classification
Exploring Sequential Learning Approaches for Optimum-Path Forest
Commercial Losses Characterization in Power Distribution Systems Using Optimum-Pat...
Grant number: | 12/06472-9 |
Support Opportunities: | Scholarships in Brazil - Doctorate |
Start date: | July 01, 2012 |
End date: | June 30, 2016 |
Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
Principal Investigator: | João Paulo Papa |
Grantee: | Daniel Osaku |
Host 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. | |
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