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Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition

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3 Author(s)
Ke Lu ; School of Computer Science and Engineering, University of Electronic Science and Technology of China , Chengdu, China ; Zhengming Ding ; Sam Ge

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.

Published in:

IEEE Transactions on Intelligent Transportation Systems  (Volume:13 ,  Issue: 4 )