Texture Discrimination via Hilbert Curve Path Based Information Quantifiers
This work addresses texture analysis for applications in image processing, but it is incremental as it combines existing methods (Hilbert curve and information quantifiers) without a major breakthrough.
The paper tackled texture classification by extracting data via the Hilbert curve and computing three information theory quantifiers, achieving discrimination based on correlation degrees and invariance to transformations, with validation on synthetic and Brodatz databases.
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.