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https://doi.org/10.1109/TITS.2012.2220965
Title: | Sparse-representation-based graph embedding for traffic sign recognition | Authors: | Lu, K. Ding, Z. Ge, S. |
Keywords: | Dimensionality reduction graph embedding machine learning sparse representation |
Issue Date: | 2012 | Citation: | Lu, K., Ding, Z., Ge, S. (2012). Sparse-representation-based graph embedding for traffic sign recognition. IEEE Transactions on Intelligent Transportation Systems 13 (4) : 1515-1524. ScholarBank@NUS Repository. https://doi.org/10.1109/TITS.2012.2220965 | Abstract: | Researchers have proposed various machine learning algorithms for traffic sign recognition, which is a supervised multicategory classification problem with unbalanced class frequencies and various appearances. We present a novel graph embedding algorithm that strikes a balance between local manifold structures and global discriminative information. A novel graph structure is designed to depict explicitly the local manifold structures of traffic signs with various appearances and to intuitively model between-class discriminative information. Through this graph structure, our algorithm effectively learns a compact and discriminative subspace. Moreover, by using L2, 1-norm, the proposed algorithm can preserve the sparse representation property in the original space after graph embedding, thereby generating a more accurate projection matrix. Experiments demonstrate that the proposed algorithm exhibits better performance than the recent state-of-the-art methods. © 2011 IEEE. | Source Title: | IEEE Transactions on Intelligent Transportation Systems | URI: | http://scholarbank.nus.edu.sg/handle/10635/83043 | ISSN: | 15249050 | DOI: | 10.1109/TITS.2012.2220965 |
Appears in Collections: | Staff Publications |
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