Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Subspace based feature selection for pattern recognition
Information Sciences: an International Journal
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
Traffic sign recognition system with β -correction
Machine Vision and Applications
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
A review of log-polar imaging for visual perception in robotics
Robotics and Autonomous Systems
A driver fatigue recognition model based on information fusion and dynamic Bayesian network
Information Sciences: an International Journal
Robust class similarity measure for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
Goal evaluation of segmentation algorithms for traffic sign recognition
IEEE Transactions on Intelligent Transportation Systems
In-vehicle camera traffic sign detection and recognition
Machine Vision and Applications
A fast VQ codebook search with initialization and search order
Information Sciences: an International Journal
Image classification by non-negative sparse coding, low-rank and sparse decomposition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Salient coding for image classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Twin Mahalanobis distance-based support vector machines for pattern recognition
Information Sciences: an International Journal
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System
IEEE Transactions on Intelligent Transportation Systems
Probabilistic support vector machines for classification of noise affected data
Information Sciences: an International Journal
Enhancing directed binary trees for multi-class classification
Information Sciences: an International Journal
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
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Recognizing traffic signs is a challenging problem; and it has captured the attention of the computer vision community for several decades. Essentially, traffic sign recognition is a multi-class classification problem that has become a real challenge for computer vision and machine learning techniques. Although many machine learning approaches are used for traffic sign recognition, they are primarily used for classification, not feature design. Identifying rich features using modern machine learning methods has recently attracted attention and has achieved success in many benchmarks. However these approaches have not been fully implemented in the traffic sign recognition problem. In this paper, we propose a new approach to tackle the traffic sign recognition problem. First, we introduce a new feature learning approach using group sparse coding. The primary goal is to exploit the intrinsic structure of the pre-learned visual codebook. This new coding strategy preserves locality and encourages similar descriptors to share similar sparse representation patterns. Second, we use a non-uniform quantization approach based on log-polar mapping. Using the log-polar mapping of the traffic sign image, rotated and scaled patterns are converted into shifted patterns in the new space. We extract the local descriptors from these patterns to learn the features. Finally, by evaluating the proposed approach using the German Traffic Sign Recognition Benchmark dataset, we show that the proposed coding strategy outperforms existing coding methods and the obtained results are comparable to the state-of-the-art.