Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25558
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Robust vehicle classification based on deep features learning
Authors: Niroomand, Naghmeh
Bach, Christian
Elser, Miriam
et. al: No
DOI: 10.1109/ACCESS.2021.3094366
10.21256/zhaw-25558
Published in: IEEE Access
Volume(Issue): 9
Page(s): 95675
Pages to: 95685
Issue Date: 2021
Publisher / Ed. Institution: IEEE
ISSN: 2169-3536
Language: English
Subjects: Automobile; Classification algorithm; Clustering algorithm; Feature extraction; Fuzzy C-means clustering; Semi-supervised learning
Subject (DDC): 006: Special computer methods
629: Aeronautical, automotive engineering
Abstract: 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/25558
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Management and Law
Organisational Unit: Center for Economic Policy (FWP)
Appears in collections:Publikationen School of Management and Law

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