A machine learning–based framework for analyzing car brand styling

Abstract

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.