W. P. Amorim, Felipe Silveira Brito Borges, Pache Marcio C. B., M. H. Carvalho, H. Pistori
{"title":"Optimum-Path Forest in the classification of defects in Bovine Leather","authors":"W. P. Amorim, Felipe Silveira Brito Borges, Pache Marcio C. B., M. H. Carvalho, H. Pistori","doi":"10.1109/WVC.2019.8876936","DOIUrl":null,"url":null,"abstract":"In this paper, the Optimum-Path Forest (OPF) classifier is applied in the classification of defects in cowhide, a problem of great evaluation complexity. The OPF classifier reduces a pattern classification problem to the problem of partitioning the vertices of a graph induced by its data set. The results revealed a competent performance compared to traditional classifiers, such as Support Vector Machines (SVM), Artificial Neural Networks-Perceptron Multilayer (MLP), Decision Trees (J48) and k-Nearest Neighbor (kNN).","PeriodicalId":144641,"journal":{"name":"2019 XV Workshop de Visão Computacional (WVC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 XV Workshop de Visão Computacional (WVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WVC.2019.8876936","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, the Optimum-Path Forest (OPF) classifier is applied in the classification of defects in cowhide, a problem of great evaluation complexity. The OPF classifier reduces a pattern classification problem to the problem of partitioning the vertices of a graph induced by its data set. The results revealed a competent performance compared to traditional classifiers, such as Support Vector Machines (SVM), Artificial Neural Networks-Perceptron Multilayer (MLP), Decision Trees (J48) and k-Nearest Neighbor (kNN).