Two new parameters for the ordinal analysis of images

Abstract

Local patterns play an important role in statistical physics as well as in image processing. Two-dimensional ordinal patterns were studied by Ribeiro et al. who determined permutation entropy and complexity in order to classify paintings and images of liquid crystals. Here we find that the 2 by 2 patterns of neighboring pixels come in three types. The statistics of these types, expressed by two parameters, contains the relevant information to describe and distinguish textures. The parameters are most stable and informative for isotropic structures.

arXiv comment20 pages, 10 figures

References in corpus (6)

• History of art paintings through the lens of entropy and complexity
• Complexity-Entropy Causality Plane as a Complexity Measure for Two-dimensional Patterns
• Structural Information in Two-Dimensional Patterns: Entropy Convergence and Excess Entropy
• Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane
• Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods
• Mapping images into ordinal networks

Cited by in corpus (5)

• Characterizing unstructured data with the nearest neighbor permutation entropy
• Two-by-two ordinal patterns in art paintings
• Permutation-Based Distances for Groups and Group-Valued Time Series
• Exploring the robustness of permutation entropy analysis to differentiate between closed-eyes and open-eyes resting states
• Interpretable liquid crystal phase classification via two-by-two ordinal patterns