Abstract
Mining discriminative spatial patterns in image data is a subject of interest in traffic sign recognition. In this paper, we use an approach for detecting spatial regions that are highly discriminative. The main idea is to search the normalized size blobs for discriminative regions by adaptively partitioning the space into progressively smaller sub-regions. Thus, each cluster of signs is characterized by an unique region pattern which consists of homogeneous and discriminant 2-D regions. The mean intensities of these regions are used as features. To evaluate the discriminative power of the attributes corresponding to detected regions, we performed classification experiments using a classifier based on Support Vector Machines. The proposed method has been tested in a real traffic sign database. Results demonstrate that the method can achieve a considerable reduction of features with respect to extraction from raw images while maintaining accurate.
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Lafuente-Arroyo, S., López-Sastre, R.J., Maldonado-Bascón, S., Martínez-Tomás, R. (2013). Discriminant Splitting of Regions in Traffic Sign Recognition. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Computation in Engineering and Medical Applications. IWINAC 2013. Lecture Notes in Computer Science, vol 7931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38622-0_41
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DOI: https://doi.org/10.1007/978-3-642-38622-0_41
Publisher Name: Springer, Berlin, Heidelberg
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