Robust Vehicle Classification Based on Deep Features Learning

IEEE Access

Miriam Elser¹  Naghmeh Niroomand¹  Christian Bach² 

[1] Automotive Powertrain Technologies Laboratory, Swiss Federal Laboratories for Materials Science and Technology, D&x00FC;bendorf, Switzerland;

关键词: Vehicle classification;    fuzzy C-means clustering;    semi-supervised learning;    feature learning;   

DOI  :  10.1109/ACCESS.2021.3094366

来源: DOAJ

【 摘 要 】

This paper aims to introduce a scientific Semi-Supervised Fuzzy C-Mean (SSFCM) clustering approach for passenger cars classification based on the feature learning technique. The proposed method is able to classify passenger vehicles in the micro, small, middle, upper middle, large and luxury classes. The performance of the algorithm is analyzed and compared with an unsupervised fuzzy C-means (FCM) clustering algorithm and Swiss expert classification dataset. Experiment results demonstrate that the classification of SSFCM algorithm has better correlation with expert classification than traditional unsupervised algorithm. These results exhibit that SSFCM can reduce the sensitivity of FCM to the initial cluster centroids with the help of labeled instances. Furthermore, SSFCM results in improved classification performance by using the resampling technique to deal with the multi-class imbalanced problem and eliminate the irrelevant and redundant features.

【 授权许可】

Unknown