The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Real-time classification of traffic signs
Real-Time Imaging
Road sign classification using Laplace kernel classifier
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Robust Real-Time Face Detection
International Journal of Computer Vision
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detection and classification of road signs in natural environments
Neural Computing and Applications
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Building Road-Sign Classifiers Using a Trainable Similarity Measure
IEEE Transactions on Intelligent Transportation Systems
Speed limit sign recognition using log-polar mapping and visual codebook
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Application of BW-ELM model on traffic sign recognition
Neurocomputing
Traffic sign recognition using group sparse coding
Information Sciences: an International Journal
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Traffic sign recognition is an example of a hard multiclass classification problem. The existing approaches to that problem typically associate with each sign class a real-valued likelihood function and assign such a label to the unknown image that maximizes the value of this function. These template-matching techniques are usually based on arbitrary similarity metrics, such as normalized cross correlation, which do not capture the characteristics of the sign imagery. In this paper, we study the concept of a robust sign similarity measure that can be inferred from the domain-specific data. Two novel machine-learning techniques are proposed as a framework for automatic construction of such a measure from the pairs of images representing either the same or different classes. One is called SimBoost, which is a variation of the AdaBoost algorithm, and the other is based on the fuzzy regression tree framework. Through the experiments with low-quality images, we show that the proposed method admits efficient road sign recognition and outperforms the existing approaches in terms of the classification accuracy.