Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition

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 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$L_{2, 1}$</tex></formula> -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.