A machine learning-based framework for analyzing car brand styling
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论文类型：期刊论文
发表时间：2018-07-10
发表刊物：ADVANCES IN MECHANICAL ENGINEERING
收录刊物：SCIE
卷号：10
期号：7
ISSN号：1687-8140
关键字：Machine learning; styling analysis; car brand styling; styling consistency; classification
摘要：To avoid the requirement of expert knowledge in conventional methods for car styling analysis, this article proposes a machine learning-based method which requires no expert-engineered features for car frontal styling analysis. In this article, we aim to identify the group behaviors in car styling such as the degree of brand styling consistency among different automakers and car styling patterns. The brand styling consistency is considered as a group behavior in this article and is formulated as a brand classification problem. This classification problem is then solved by a machine learning method based on the PCANet for automatic feature encoding and the support vector machine for feature-based classification. The brand styling consistency can thus be measured based on the classification accuracy. To perform the analysis, a car frontal styling database with 23 brands is first built. To present discovered brand styling patterns in classification, a decoding method is proposed to map salient features for brand classification to original images for revelation of salient styling regions. To provide a direct perception in brand styling characteristics, frontal styling representatives of several brands are present as well. This study contributes to efficient identification of brand styling consistency and visualization of brand styling patterns without relying on expert experience.
发表时间：2018-07-10