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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 4/2010

01.06.2010 | Original Article

Emergent self-organizing feature map for recognizing road sign images

verfasst von: Yok-Yen Nguwi, Siu-Yeung Cho

Erschienen in: Neural Computing and Applications | Ausgabe 4/2010

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Abstract

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.
Vorheriger Artikel A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line
Nächster Artikel Face recognition based on gradient gabor feature and Efficient Kernel Fisher analysis

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Metadaten
Titel
Emergent self-organizing feature map for recognizing road sign images
verfasst von
Yok-Yen Nguwi
Siu-Yeung Cho
Publikationsdatum
01.06.2010
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 4/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0315-6

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