Classification of Repetitive Patterns Using Symmetry Group Prototypes

Abstract

[EN] We present a novel computational framework for automatic classification method by symmetries, for periodic images applied to content based image retrieval. The existing methods have several drawbacks because of the use of heuristics. These methods have shown low classification values when images exhibit imperfections due to the fabrication or the hand made process. Also, there is no way to give some computation of the classification goodness-of-fit. We propose to obtain an automatic parameter estimation for symmetry analysis. Thus, the image classification is redefined as distances computation to the prototypes of a set of defined classes. Our experimental results improves the state of the art in wallpaper classification methods.