Please use this identifier to cite or link to this item:
Title: Sparse-representation-based graph embedding for traffic sign recognition
Authors: Lu, K.
Ding, Z.
Ge, S. 
Keywords: Dimensionality reduction
graph embedding
machine learning
sparse representation
Issue Date: 2012
Citation: Lu, K., Ding, Z., Ge, S. (2012). Sparse-representation-based graph embedding for traffic sign recognition. IEEE Transactions on Intelligent Transportation Systems 13 (4) : 1515-1524. ScholarBank@NUS Repository.
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 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. © 2011 IEEE.
Source Title: IEEE Transactions on Intelligent Transportation Systems
ISSN: 15249050
DOI: 10.1109/TITS.2012.2220965
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Feb 3, 2023


checked on Feb 3, 2023

Page view(s)

checked on Feb 2, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.