用希尔伯特曲线分析纹理，可区分相关性差异的图像。
Texture Discrimination via Hilbert Curve Path Based Information Quantifiers
- 通过希尔伯特曲线提取图像数据，计算三种信息量度。
- 能区分不同相关程度的纹理，对旋转不变。
- 适用于黑白与彩色图像，适合纹理分类任务。
颜色空间分布及图形粗糙/光滑特征的分析具有广泛应用价值。本文提出一种基于希尔伯特曲线的纹理分类方法：先将图像数据沿希尔伯特曲线提取，再计算三种信息论量度——排列熵、排列复杂度和费舍尔信息度量。该方法具备重要特性：(i) 可根据霍斯特指数衡量的相关性差异区分图像；(ii) 对旋转与对称变换保持不变性；(iii) 适用于黑白与彩色图像。验证不仅使用合成图像，还基于著名的Brodatz图像数据库进行。
原文摘要 · Abstract (English)
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.
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