An Analysis of the Road Signs Classification Based on the Higher-Order Singular Value Decomposition of the Deformable Pattern Tensors

The paper presents a framework for classification of rigid objects in digital images. It consists of a generator of the geometrically deformed prototypes and an ensemble of classifiers. The role of the former is to provide a sufficient training set for subsequent classification of deformed objects in real conditions. This is especially important in cases of a limited number of available prototype exemplars. Classification is based on the Higher-Order Singular Value Decomposition of tensors composed from the sets of deformed prototypes. Construction of such deformable tensors is flexible and can be done independently for each object. They can be obtained either from a single prototype, which is then affinely deformed, or from many real exemplars, if available. The method was tested in the task of recognition of the prohibition road signs. Experiments with real traffic scenes show that the method is characteristic of high speed and accuracy for objects seen under different viewpoints. Implementation issues of tensor decompositions are also discussed.