Susi Marianingsih, Fitri Utaminingrum
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. © 2018 IEEE.
Computer Vision Research Group, Faculty of Computer Science, Brawijaya University, Malang, Indonesia
