Texture Discrimination via Hilbert Curve Path Based Information Quantifiers

TL;DR

This paper introduces a novel texture classification method using Hilbert curve-based data extraction and information theory quantifiers, demonstrating invariance to transformations and effectiveness on synthetic and real images.

Contribution

It presents a new texture analysis approach combining Hilbert curve paths with information theory measures, enhancing discrimination and invariance properties.

Findings

Effective discrimination of textures based on correlation levels.

Invariant to rotation and symmetry transformations.

Validated on synthetic and Brodatz image datasets.

Abstract

The analysis of the spatial arrangement of colors and roughness/smoothness of figures is relevant due to its wide range of applications. This paper proposes a texture classification method that extracts data from images using the Hilbert curve. Three information theory quantifiers are then computed: permutation entropy, permutation complexity, and Fisher information measure. The proposal exhibits some important properties: (i) it allows to discriminate figures according to varying degrees of correlations (as measured by the Hurst exponent), (ii) it is invariant to rotation and symmetry transformations, (iii) it can be used either in black and white or color images. Validations have been made not only using synthetic images but also using the well-known Brodatz image database.