Wavelet Domain Multifractal Analysis for Static and Dynamic Texture Classification

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.