Symmetry Computation in Repetitive Images Using Minimum-Variance Partitions

Abstract

The symmetry computation has recently been recognized as a topic of interest in many different fields of computer vision and image analysis, which still remains as an open problem. In this work we propose an unified method to compute image symmetries based on finding the minimum-variance partitions of the image that best describe its repetitive nature. We then use a statistical measurement of these partitions as symmetry score. The principal idea is that the same measurement can be used to score symmetries (rotation, reflection, and glide reflection). Finally, a feature vector composed from these symmetry values is used to classify the whole image according to a symmetry group. An increase in the success rate, compared to other reference methods, indicates the improved discriminative capabilities of the proposed symmetry features. Our experimental results improve the state of the art in wallpaper classification methods.