Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition
Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition
复制标题
用于交通标志识别的基于稀疏表示的图嵌入
DOI：
10.1109/tits.2012.2220965
复制
发表时间：
2012-12-01
影响因子：
8.5
通讯作者：
Ge, Sam
中科院分区：
文献类型：
--
作者：
Lu, Ke;Ding, Zhengming;Ge, Sam
研究人员已经提出了各种机器学习算法用于交通标志识别，这是一个有监督的多类别分类问题，具有不平衡的类别频率和各种外观。我们提出了一种新的图嵌入算法，在局部流形结构和全局判别信息之间取得了平衡。设计了一种新的图结构，以明确地描述具有不同外观的交通标志的局部流形结构，并直观地建模类间判别信息。通过这种图结构，我们的算法有效地学习一个紧凑的和有区别的子空间。此外，通过使用L-2，L-1-范数，该算法能够在图嵌入后保持原空间的稀疏表示性质，从而生成更精确的投影矩阵。实验表明，该算法具有更好的性能比最近的国家的最先进的方法。
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.