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https://doi.org/10.21256/zhaw-25558| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Robust vehicle classification based on deep features learning |
| Autor/-in: | Niroomand, Naghmeh Bach, Christian Elser, Miriam |
| et. al: | No |
| DOI: | 10.1109/ACCESS.2021.3094366 10.21256/zhaw-25558 |
| Erschienen in: | IEEE Access |
| Band(Heft): | 9 |
| Seite(n): | 95675 |
| Seiten bis: | 95685 |
| Erscheinungsdatum: | 2021 |
| Verlag / Hrsg. Institution: | IEEE |
| ISSN: | 2169-3536 |
| Sprache: | Englisch |
| Schlagwörter: | Automobile; Classification algorithm; Clustering algorithm; Feature extraction; Fuzzy C-means clustering; Semi-supervised learning |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 629: Luftfahrt- und Fahrzeugtechnik |
| Zusammenfassung: | 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 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Management and Law |
| Organisationseinheit: | Zentrum für Arbeitsmärkte, Digitalisierung und Regionalökonomie (CLDR) |
| Enthalten in den Sammlungen: | Publikationen School of Management and Law |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Niroomand-etal_Robust-vehicle-classification-deep-features-learning.pdf | 6.9 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Niroomand, N., Bach, C., & Elser, M. (2021). Robust vehicle classification based on deep features learning. IEEE Access, 9, 95675–95685. https://doi.org/10.1109/ACCESS.2021.3094366
Niroomand, N., Bach, C. and Elser, M. (2021) ‘Robust vehicle classification based on deep features learning’, IEEE Access, 9, pp. 95675–95685. Available at: https://doi.org/10.1109/ACCESS.2021.3094366.
N. Niroomand, C. Bach, and M. Elser, “Robust vehicle classification based on deep features learning,” IEEE Access, vol. 9, pp. 95675–95685, 2021, doi: 10.1109/ACCESS.2021.3094366.
NIROOMAND, Naghmeh, Christian BACH und Miriam ELSER, 2021. Robust vehicle classification based on deep features learning. IEEE Access. 2021. Bd. 9, S. 95675–95685. DOI 10.1109/ACCESS.2021.3094366
Niroomand, Naghmeh, Christian Bach, and Miriam Elser. 2021. “Robust Vehicle Classification Based on Deep Features Learning.” IEEE Access 9: 95675–85. https://doi.org/10.1109/ACCESS.2021.3094366.
Niroomand, Naghmeh, et al. “Robust Vehicle Classification Based on Deep Features Learning.” IEEE Access, vol. 9, 2021, pp. 95675–85, https://doi.org/10.1109/ACCESS.2021.3094366.
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