Building Road-Sign Classifiers Using a Trainable Similarity Measure

Deriving an informative data representation is an important prerequisite when designing road-sign classifiers. A frequently used strategy for road-sign classification is based on the normalized cross correlation similarity to class prototypes followed by the nearest neighbor classifier. Because of the global nature of the cross correlation similarity, this method suffers from presence of uninformative pixels (caused, e.g., by occlusions) and is computationally demanding. In this paper, a novel concept of a trainable similarity measure is introduced, which alleviates these shortcomings. The similarity is based on individual matches in a set of local image regions. The set of regions that are relevant for a particular similarity assessment is refined by the training process. It is illustrated on a set of experiments with road-sign-classification problems that the trainable similarity yields high-performance data representations and classifiers. Apart from a multiclass classification accuracy, nonsign rejection capability and computational demands in execution are also discussed. It appears that the trainable similarity representation alleviates some difficulties of other algorithms that are currently used in road-sign classification