Department of Mathematics, National University of Singapore, Singapore.
IEEE Trans Image Process. 2013 Jan;22(1):286-99. doi: 10.1109/TIP.2012.2214040. Epub 2012 Aug 17.
In this paper, we propose a new texture descriptor for both static and dynamic textures. The new descriptor is built on the wavelet-based spatial-frequency analysis of two complementary wavelet pyramids: standard multiscale and wavelet leader. These wavelet pyramids essentially capture the local texture responses in multiple high-pass channels in a multiscale and multiorientation fashion, in which there exists a strong power-law relationship for natural images. Such a power-law relationship is characterized by the so-called multifractal analysis. In addition, two more techniques, scale normalization and multiorientation image averaging, are introduced to further improve the robustness of the proposed descriptor. Combining these techniques, the proposed descriptor enjoys both high discriminative power and robustness against many environmental changes. We apply the descriptor for classifying both static and dynamic textures. Our method has demonstrated excellent performance in comparison with the state-of-the-art approaches in several public benchmark datasets.
在本文中，我们提出了一种新的纹理描述符，适用于静态和动态纹理。新描述符基于两个互补的小波金字塔（标准多尺度和小波引导）的基于小波的空间频率分析构建：标准多尺度和小波引导。这些小波金字塔本质上以多尺度和多方向的方式在多个高通通道中捕获局部纹理响应，其中存在自然图像的强幂律关系。这种幂律关系的特点是所谓的多重分形分析。此外，还引入了另外两种技术，即尺度归一化和多方向图像平均化，以进一步提高所提出描述符的稳健性。通过结合这些技术，所提出的描述符具有较高的判别能力和对许多环境变化的鲁棒性。我们将该描述符应用于静态和动态纹理的分类。与几个公共基准数据集的最新方法相比，我们的方法表现出了优异的性能。
