Efficiently representing spatio-temporal features of dynamic textures (DTs) in videos has been restricted due to negative impacts of the well-known issues of environmental changes, illumination, and noise. In order to mitigate those, this paper proposes a new approach for an efficient DT representation by addressing the following novel concepts. Firstly, a novel filtering kernel, called Difference of Derivative Gaussians (DoDG), is introduced for the first time based on high-order derivative of a Gaussian kernel. It allows to point out DoDG-based filtered outcomes which are prominently resistant to noise for DT representation compared to exploiting the conventional Difference of Gaussians (DoG). A new framework in low computational complexity is then presented to take DoDG into account video denoising as an effective preprocessing of DT encoding. Finally, a simple variant of Local Binary Patterns (LBPs) is addressed to extract local features from these DoDG-filtered outcomes for constructing discriminative DoDG-based descriptors in small dimension, expected as one of appreciated solutions for mobile applications. Experimental results for DT recognition have verified that our proposal significantly performs well compared to all non-deep-learning methods, while being very close to deep-learning approaches. Also, ours are eminently better than those based on the traditional DoG.

由于众所周知的环境变化、光照和噪声问题的负面影响，在视频中有效表示动态纹理 (DT) 的时空特征受到限制。为了减轻这些问题，本文提出了一种通过解决以下新概念来实现高效 DT 表示的新方法。首先，基于高斯核的高阶导数，首次引入了一种新的滤波核，称为微分高斯（DoDG）。它允许指出基于 DoDG 的过滤结果，与利用传统的高斯差分 (DoG) 相比，这些结果对 DT 表示的噪声具有显着的抵抗力。然后提出了一种低计算复杂度的新框架，将 DoDG 考虑到视频去噪作为 DT 编码的有效预处理。最后，局部二进制模式 (LBP) 的一个简单变体用于从这些 DoDG 过滤的结果中提取局部特征，以构建小维度的基于 DoDG 的判别描述符，有望成为移动应用程序的一种受欢迎的解决方案。DT 识别的实验结果已经证实，与所有非深度学习方法相比，我们的提议明显表现良好，同时与深度学习方法非常接近。此外，我们的性能明显优于基于传统 DoG 的性能。DT 识别的实验结果已经证实，与所有非深度学习方法相比，我们的提议明显表现良好，同时与深度学习方法非常接近。此外，我们的性能明显优于基于传统 DoG 的性能。DT 识别的实验结果已经证实，与所有非深度学习方法相比，我们的提议明显表现良好，同时与深度学习方法非常接近。此外，我们的性能明显优于基于传统 DoG 的性能。