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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.
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
ISSN: 15249050
DOI: 10.1109/TITS.2012.2220965
Appears in Collections:Staff Publications

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