Optimizing optimum-path forest classification for huge datasets

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorPapa, João Paulo
dc.creatorCappabianco, Fábio A. M.
dc.creatorFalcão, Alexandre X.
dc.date2014-05-27T11:24:50Z
dc.date2016-10-25T18:30:20Z
dc.date2014-05-27T11:24:50Z
dc.date2016-10-25T18:30:20Z
dc.date2010-11-18
dc.date.accessioned2017-04-06T01:43:33Z
dc.date.available2017-04-06T01:43:33Z
dc.identifierProceedings - International Conference on Pattern Recognition, p. 4162-4165.
dc.identifier1051-4651
dc.identifierhttp://hdl.handle.net/11449/71961
dc.identifierhttp://acervodigital.unesp.br/handle/11449/71961
dc.identifier10.1109/ICPR.2010.1012
dc.identifier2-s2.0-78149477256
dc.identifierhttp://dx.doi.org/10.1109/ICPR.2010.1012
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/892875
dc.descriptionTraditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.
dc.languageeng
dc.relationProceedings - International Conference on Pattern Recognition
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBrain image classification
dc.subjectOptimum-Path forest
dc.subjectSupervised classification
dc.subjectSupport Vector machines
dc.subjectAutomatic recognition
dc.subjectBrain images
dc.subjectComputational costs
dc.subjectData sets
dc.subjectForest classification
dc.subjectGray matter
dc.subjectHuman brain
dc.subjectLarge datasets
dc.subjectMagnetic resonance images
dc.subjectPattern recognition techniques
dc.subjectRecognition rates
dc.subjectSupport vector
dc.subjectWhite matter
dc.subjectImage analysis
dc.subjectImage classification
dc.subjectMagnetic resonance
dc.subjectMagnetic resonance imaging
dc.subjectSupport vector machines
dc.subjectClassification (of information)
dc.titleOptimizing optimum-path forest classification for huge datasets
dc.typeOtro


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