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New trends on optimum-path forest-based pattern recognition

Grant number: 09/16206-1
Support type:Research Grants - Young Investigators Grants
Duration: March 01, 2010 - February 28, 2014
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:João Paulo Papa
Grantee:João Paulo Papa
Home Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
Assoc. researchers:Alexandre Xavier Falcão ; Aparecido Nilceu Marana
Associated grant(s):14/02424-5 - 22nd International Conference on Pattern Recognition, AR.EXT
12/13988-1 - The 37th IEEE conference on local computer networks, AR.EXT
Associated scholarship(s):13/20387-7 - Hyperparameter optimization in deep learning arquitectures, BE.PQ
12/06472-9 - Exploring contextual classification approaches for Optimum-Path Forest, BP.DR
11/14058-5 - Exploring sequential learning approaches for Optimum-Path Forest, BP.MS
+ associated scholarships 11/14094-1 - Exploring Multi-labeling approaches by Optimum-Path Forest, BP.MS
11/11777-0 - Descriptor combination using Harmony Search and Optimum-Path Forest, BP.IC
10/12222-0 - Automatic aquatic weed classification using shape analysis and Optimum-Path Forest, BP.IC
10/12697-8 - On the implementation of the Optimum-Path Forest training algorithm in GPU, BP.MS
10/02045-3 - Intrusion detection based on Optimum-Path Forest, BP.MS - associated scholarships

Abstract

Traditional pattern classifiers, such as Support Vector Machines (SVM) and neural networks, pay the price for an expensive training phase to achieve acceptable recognition rates in a test set. Thus, these techniques are inviable in situations that require a permanent data retraining, and mainly in which we have large datasets (interactive segmentation of magnetic resonance images of the brain and segmentation of ferrous allow samples obtained from high resolution metallographic images, for instance). Recently, a new pattern recognition algorithm called Optimum-Path Forest was proposed in the literature, which has been demonstrated to be superior than artificial neural networks and bayesian classifiers, and similar to SVM, but extremely faster (500x-1000x, depending on the dataset size). The OPF also received 3 prizes in 2009. The OPF classifier models the data classification task as a partition problem in a graph induced by the feature space into optimum-path trees (OPTs), in which each sample is stronger connected to the root of its tree than to any other root in this forest. Samples that belong to the same OPT receive the same label in the data classification process. Although OPF classifier has been used in several research topics in the last 2 years, such as remote sensing, computer vision (digital fingerprint and face recognition), automatic identification of human parasites and biomedical signal processing, there exists many others research areas that need to validate the OPF applicability. This research project has as the main goal a wide and complete study about OPF classifier, as well the development of its new variants, its implementation in GPU (Graphics Processing Unit), and to validate the OPF applicability in other research topics and in situations that require large datasets, which cannot be solved with both efficiency and effectiveness by the traditional pattern recognition methods, such as neural networks and SVM. This project also aims to apply OPF for object tracking and signal processing. Cooperations with several national and international research groups working with the same objective, i.e., to divulge and to validate the OPF classifier, will be addressed. Recall that all proposed works inside this project are innovative, due to the fact of each one of them to address one research topic that was not already explored by the OPF classifier. This research project address activities in several research levels, such as undergraduate and graduate studies. (AU)

Scientific publications (13)
(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)
PEREIRA, LUIS A. M.; PAPA, JOAO P.; COELHO, ANDRE L. V.; LIMA, CLODOALDO A. M.; PEREIRA, DANILLO R.; DE ALBUQUERQUE, VICTOR HUGO C. Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms. NEURAL COMPUTING & APPLICATIONS, v. 31, n. 2, p. 1317-1329, FEB 2019. Web of Science Citations: 0.
PAPA, JOAO PAULO; NACHIF FERNANDES, SILAS EVANDRO; FALCAO, ALEXANDRE XAVIER. Optimum-Path Forest based on k-connectivity: Theory and applications. PATTERN RECOGNITION LETTERS, v. 87, n. SI, p. 117-126, FEB 1 2017. Web of Science Citations: 16.
PASSOS JUNIOR, LEANDRO APARECIDO; OBA RAMOS, CAIO CESAR; RODRIGUES, DOUGLAS; PEREIRA, DANILLO ROBERTO; DE SOUZA, ANDRE NUNES; PONTARA DA COSTA, KELTON AUGUSTO; PAPA, JOAO PAULO. Unsupervised non-technical losses identification through optimum-path forest. Electric Power Systems Research, v. 140, p. 413-423, NOV 2016. Web of Science Citations: 13.
PEREIRA, CLAYTON R.; PEREIRA, DANILO R.; SILVA, FRANCISCO A.; MASIEIRO, JOAO P.; WEBER, SILKE A. T.; HOOK, CHRISTIAN; PAPA, JOAO P. A new computer vision-based approach to aid the diagnosis of Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 136, p. 79+, NOV 2016. Web of Science Citations: 16.
PIRES, RAFAEL G.; PEREIRA, DANILLO R.; PEREIRA, LUIS A. M.; MANSANO, ALEX F.; PAPA, JOO P. Projections onto convex sets parameter estimation through harmony search and its application for image restoration. NATURAL COMPUTING, v. 15, n. 3, SI, p. 493-502, SEP 2016. Web of Science Citations: 3.
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.
COSTA, KELTON A. P.; PEREIRA, LUIS A. M.; NAKAMURA, RODRIGO Y. M.; PEREIRA, CLAYTON R.; PAPA, JOAO P.; FALCAO, ALEXANDRE XAVIER. A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks. INFORMATION SCIENCES, v. 294, p. 95-108, FEB 10 2015. Web of Science Citations: 30.
PISANI, RODRIGO JOSE; MIZOBE NAKAMURA, RODRIGO YUJI; RIEDEL, PAULINA SETTI; LOPES ZIMBACK, CELIA REGINA; FALCAO, ALEXANDRE XAVIER; PAPA, JOAO PAULO. Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, v. 52, n. 10, p. 6075-6085, OCT 2014. Web of Science Citations: 9.
NUNES, THIAGO M.; COELHO, ANDRE L. V.; LIMA, CLODOALDO A. M.; PAPA, JOAO P.; DE ALBUQUERQUE, VICTOR HUGO C. EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment. Neurocomputing, v. 136, p. 103-123, JUL 20 2014. Web of Science Citations: 50.
NAKAMURA, RODRIGO Y. M.; GARCIA FONSECA, LEILA MARIA; DOS SANTOS, JEFERSSON ALEX; TORRES, RICARDO DA S.; YANG, XIN-SHE; PAPA, JOAO PAPA. Nature-Inspired Framework for Hyperspectral Band Selection. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, v. 52, n. 4, p. 2126-2137, APR 2014. Web of Science Citations: 31.
LUZ, EDUARDO JOSE DA S.; NUNES, THIAGO M.; DE ALBUQUERQUE, VICTOR HUGO C.; PAPA, JOAO P.; MENOTTI, DAVID. ECG arrhythmia classification based on optimum-path forest. EXPERT SYSTEMS WITH APPLICATIONS, v. 40, n. 9, p. 3561-3573, JUL 2013. Web of Science Citations: 57.
NUNES, THIAGO M.; DE ALBUQUERQUE, VICTOR HUGO C.; PAPA, JOAO P.; SILVA, CLEITON C.; NORMANDO, PAULO G.; MOURA, ELINEUDO P.; TAVARES, JOAO MANUEL R. S. Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals. EXPERT SYSTEMS WITH APPLICATIONS, v. 40, n. 8, p. 3096-3105, JUN 15 2013. Web of Science Citations: 24.
SUZUKI, CELSO T. N.; GOMES, JANCARLO F.; FALCAO, ALEXANDRE X.; PAPA, JOAO P.; HOSHINO-SHIMIZU, SUMIE. Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images. IEEE Transactions on Biomedical Engineering, v. 60, n. 3, p. 803-812, MAR 2013. Web of Science Citations: 24.

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