PyOPF: pattern classification based on Optimum-Path Forest on Python

• Grant number: 17/10537-2
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2017
• End date: July 31, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Alexandre Xavier Falcão
• Grantee: João Paulo do Carmo de Freitas Penalber
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?, AP.TEM

Abstract

Python is a interpreted language that relies on a considerable number of computational methods, developed by many teams all around the world and made available through toolboxes. When those methods are combined in the python environment, they can be used to resolve problems from many areas. This project aims to create a python toolbox, denominated PyOPF, to teach and develop pattern classifiers based on optimum path forests. Even though python already counts with several pattern classification techniques, depending on the application, a technique may be more appropriate than another. The proposed toolbox includes multidimensional data visualization techniques for the better understanding of the user about the design of pattern classifiers based on optimum path forests. The release of this framework in python will also beneficiate its propagation.