牛皮革缺陷分类中的最优路径森林

2019 XV Workshop de Visão Computacional (WVC) Pub Date : 2019-09-01 DOI:10.1109/WVC.2019.8876936

W. P. Amorim, Felipe Silveira Brito Borges, Pache Marcio C. B., M. H. Carvalho, H. Pistori

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引用次数: 1

摘要

本文将最优路径森林(OPF)分类器应用于牛皮缺陷的分类中，这是一个评价复杂度很高的问题。OPF分类器将模式分类问题简化为由其数据集引起的图的顶点划分问题。结果显示，与支持向量机(SVM)、多层人工神经网络感知器(MLP)、决策树(J48)和k-最近邻(kNN)等传统分类器相比，该分类器具有较强的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Optimum-Path Forest in the classification of defects in Bovine Leather

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).

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来源期刊

2019 XV Workshop de Visão Computacional (WVC)

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