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 $L_{2, 1}$-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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition


    Contributors:
    Lu, Ke (author) / Ding, Zhengming (author) / Ge, Sam (author)


    Publication date :

    2012-12-01


    Size :

    1058990 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic Sign Recognition Using Sparse Representations and Active Contour Models

    Okyere, Adu-Gyamfi Yaw / Attoh-Okine, Nii | Transportation Research Record | 2014


    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    SHINOMIYA TERUHIKO | European Patent Office | 2017

    Free access

    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    MIYASATO KAZUHIRO / KOYASU TOSHIYA | European Patent Office | 2023

    Free access

    Traffic Sign Recognition

    chiluka, srikanthreddy | BASE | 2021

    Free access