Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition

作者：Lu, Ke^*; Ding, Zhengming; Ge, Sam

来源：IEEE Transactions on Intelligent Transportation Systems, 2012, 13(4): 1515-1524.

DOI：10.1109/TITS.2012.2220965

• Dimensionality reduction
• graph embedding
• machine learning
• sparse representation

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 L-2,L-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.

• 出版日期2012-12
• 单位电子科技大学