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| A Vehicle Recognition Method Based on Kernel K-SVD and Sparse Representation |
| SUN Rui,WANG Jing-Jing |
| School of Computer and Information, Hefei University of Technology, Hefei 230009 |
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Abstract The classification and recognition of vehicle is of great importance in the research of intelligent transportation system. A method based on PCA, kernel K-SVD and sparse representation classification method is proposed for two-class supervised classification. Firstly, PCA is used in this method to train both vehicle and non-vehicle images for feature extraction and dimensionality reduction. Then, the Gaussian-Kernel function is used to map the matrix to the high-dimensional space, and K-SVD is applied to train the high-dimension feature matrix for two corresponding dictionaries in this space. Finally, training images based on l1-minimization sparse coefficient are used to linearly represent test images. The experiments are carried out and the results show that the performance of the proposed method on the partially-covered case is obviously better than that of other classical methods.
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Received: 04 June 2013
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2014, Vol. 27 
