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Robust Vehicle Classification Based on Deep Features Learning
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DOI：10.1109/ACCESS.2021.3094366.png)
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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.
Keyword:
Classification algorithms
Clustering algorithms
Feature extraction
Automobiles
Vehicle detection
Semisupervised learning
Prediction algorithms
Vehicle classification
fuzzy C-means clustering
semi-supervised learning
feature learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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分享收藏分享收藏A simple generalisation of the area under the ROC curve for multiple class classification problems多类分类问题的ROC曲线下面积的简单概括.png)
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