摘要
The features extracted by principle component analysis(PCA) are the best descriptive and the features extracted by linear discriminant analysis(LDA) are the most classifiable. In this paper, these two methods are combined and a PC-LDA approach is used to extract the features of traffic signs. After obtaining the binary images of the traffic signs through normalization and binarization, PC-LDA can extract the feature subspace of the traffic sign images with the best description and classification. The extracted features are recognized by using the minimum distance classifier. The approach is verified by using MPEG7 CE Shape-1 Part-B computer shape library and traffic sign image library which includes both standard and natural traffic signs. The results show that under the condition that the traffic sign is in a nature scene, PC-LDA approach applied to binary images in which shape features are extracted can obtain better results.
The features extracted by principle component analysis （PCA） are the best descriptive and the features extracted by linear discriminant analysis （LDA） are the most classifiable. In this paper, these two methods are combined and a PC-LDA approach is used to extract the features of traffic signs. After obtaining the binary images of the traffic signs through normalization and binarization, PC-LDA can extract the feature subspace of the traffic sign images with the best description and classification. The extracted features are recognized by using the minimum distance classifier. The approach is verified by using MPEG7 CE Shape-1 Part-B computer shape library and traffic sign image library which includes both standard and natural traffic signs. The results show that under the condition that the traffic sign is in a nature scene, PC-LDA approach applied to binary images in which shape features are extracted can obtain better results.
基金
Supported by National Natural Science Foundation of China(No.61540069)
