Emergent self-organizing feature map for recognizing road sign images

Road sign recognition system remains a challenging part of designing an Intelligent Driving Support System. While there exist many approaches to classify road signs, none have adopted an unsupervised approach. This paper proposes a way of Self-Organizing feature mapping for recognizing a road sign. The emergent self-organizing map (ESOM) is employed for the feature mapping in this study. It has the capability of visualizing the distance structures as well as the density structure of high-dimensional data sets, in which the ESOM is suitable to detect non-trivial cluster structures. This paper discusses the usage of ESOM for road sign detection and classification. The benchmarking against some other commonly used classifiers was performed. The results demonstrate that the ESOM approach outperforms the others in conducting the same simulations of the road sign recognition. We further demonstrate that the result obtained with ESOM is significantly more superior than traditional SOM which does not take into the boundary effect like ESOM did.