Vehicle Image Classification Using Data Mining Techniques

被引：0

作者：

Suguitan, Agnes S. ^{[1
]}

Dacaymat, Lucille N. ^{[1
]}

机构：

[1] Don Mariano Marcos Mem State Univ, Coll Comp Sci, South Union Campus, Bacnotan, Philippines

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (CSSE 2019) | 2019年

关键词：

Data Mining; Image Preprocessing; Vehicle Classification; Classifiers; Weka;

D O I：

10.1145/3339363.3339366

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper focuses on the application of different data mining techniques to classify images of vehicles into three classes using Weka. The dataset used for this study were collected from Google Image search engine and other dataset websites. In preprocessing the images, filters such as Color Layout, Edge Histogram and Pyramid Histogram of Oriented Gradients were explored to extract the image features from the dataset. Classification techniques such as Multilayer Perceptron, Sequential Minimal Optimization, Logistic Model Trees, Simple Logistic and Random Forest were used. Results of the study showed that the edge histogram features provided much information to the classifiers in order to correctly classify the images. The SMO classifier performs best with the highest accuracy of 82.37%.

引用

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