Understanding dynamic textures (DTs) is a challenge in various computer vision applications due to the negative impacts of noise, changes of environment, illumination, and scales on capturing turbulent characteristics. In this work, we propose an efficient shallow framework for DT representation by addressing the following novel concepts. First, it is the first time in DT analysis that 2D/3D Gaussian-gradient filterings are taken into account as a pre-processing step to point out robust components against those influences in effect. Second, high-order partial derivatives of the Gaussian kernels and their informative magnitudes are exploited to forcefully capture multi-order Gaussian-gradient features. Third, these gradient kernels are investigated in multi-scale analysis of different orders and standard deviations in order to enrich more useful scale-gradient information. Finally, the obtained complementary components are shallowly encoded using a simple local operator to construct robust descriptors of High-order 2D/3D Gaussian-gradient-based Features (HoGF2D/3D\mathrm{HoGF}^{2D/3D}) against the well-known issues of DT description. Experiments for DT classification on various benchmarks have validated the interest of our approach since its performance is comparable to state-of-the-art results, including that of deep-learning methods, while it only has a small dimension.

中文翻译：

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基于高阶高斯梯度的动态纹理的突出局部表示


由于噪声、环境变化、照明和尺度对捕获湍流特征的负面影响，理解动态纹理 (DT) 是各种计算机视觉应用中的一个挑战。在这项工作中，我们通过解决以下新概念，提出了一种有效的 DT 表示浅层框架。首先，这是在 DT 分析中首次将 2D/3D 高斯梯度滤波作为预处理步骤来考虑，以指出针对这些实际影响的稳健组件。其次，利用高斯核的高阶偏导数及其信息量来强制捕获多阶高斯梯度特征。第三，对这些梯度核进行不同阶数和标准差的多尺度分析，以丰富更有用的尺度梯度信息。最后，使用简单的局部算子对获得的互补分量进行浅层编码，以构建针对井的高阶 2D/3D 高斯梯度特征 (HoGF2D/3D\mathrm{HoGF}^{2D/3D}) 的鲁棒描述符-DT描述的已知问题。在各种基准上进行的 DT 分类实验验证了我们方法的兴趣，因为它的性能可与最先进的结果（包括深度学习方法的结果）相媲美，而它的维度很小。