Car Types and Semantics Classification Using Weka

Hung-Hsiang Wang, Yunpeng Shen, Yun-Yun Hung
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Abstract

This paper presents a method of using Weka, a machine learning tool, to identify the difference of types and product semantics between fuel vehicles and electric vehicles. Pictures of 58 fuel vehicles and 42 pictures of electric vehicles during time period from 2021 to 2023 are selectively collected from Consumer Reports website to build the dataset. The fuel vehicle brands include Audi, BMW, Cadillac, and Lexus with 3 types, namely, SUV, Sedan and Luxury. In addition to the above-mentioned brands, Tesla is the 5th brand of electric vehicles. The perception of each picture is labelled by questionnaires of Automotive Model Semantics. Results reveal that even the smaller dataset can be trained to have highly accuracy models for classifying fuel vehicles and electric vehicles, different types as well as product semantics. The method presented is promising for studying car styling and exploring new applications of image classification to branding and product design.
使用Weka的汽车类型和语义分类
本文提出了一种利用机器学习工具Weka识别燃油车和电动车类型和产品语义差异的方法。有选择地从消费者报告网站上收集了2021 - 2023年期间58辆燃油车和42辆电动汽车的图片来构建数据集。燃油车品牌包括奥迪、宝马、凯迪拉克、雷克萨斯,共有SUV、轿车、豪华3个车型。除上述品牌外,特斯拉是第五大电动汽车品牌。通过汽车模型语义问卷对每个图像的感知进行标注。结果表明,即使是较小的数据集也可以训练成具有高度准确性的模型,用于对燃油汽车和电动汽车、不同类型以及产品语义进行分类。该方法对研究汽车造型、探索图像分类在品牌和产品设计中的新应用具有重要意义。
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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