Comparison of Support Vector Machine Classifier and Naive Bayes Classifier on Road Surface Type Classification

被引：0

作者：

Marianingsih, Susi ^{[1
]}

Utaminingrum, Fitri ^{[1
]}

机构：

[1] Brawijaya Univ, Comp Vis Res Grp, Fac Comp Sci, Malang, Indonesia

来源：

PROCEEDINGS OF 2018 3RD INTERNATIONAL CONFERENCE ON SUSTAINABLE INFORMATION ENGINEERING AND TECHNOLOGY (SIET 2018) | 2018年

关键词：

Texture analysis; GLCM; SVM; Naive Bayes;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This study describes the comparison of road surface classification results using Support Vector Machine (SVM) classification and Naive Bayes classification. The dataset in this study is a collection of sub-images (750 images) from the road area of the image from Google Street View. From the dataset, 600 images as data training and 150 images as data testing. Texture features are extracted from road surface images in the dataset and then we build SVM and Naive Bayes classifiers to classify road surface images in 3 categories, asphalt, gravel, and paving. Evaluation of performance classification using precision, recall, f-measure, and accuracy. The results show that SVM classifier accuracy better than Naive Bayes classifier.

引用

页码：48 / 53

页数：6

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