Understanding the chaotic motions of dynamic textures (DTs) is a challenging problem of video representation for different tasks in computer vision. This paper presents a new approach for an efficient DT representation by addressing the following novel concepts. First, a model of moment volumes is introduced as an effective pre-processing technique for enriching the robust and discriminative information of dynamic voxels with low computational cost. Second, two important extensions of Local Derivative Pattern operator are proposed to improve its performance in capturing directional features. Third, we present a new framework, called Momental Directional Patterns, taking into account the advantages of filtering and local-feature-based approaches to form effective DT descriptors. Furthermore, motivated by convolutional neural networks, the proposed framework is boosted by utilizing more global features extracted from max-pooling videos to improve the discrimination power of the descriptors. Our proposal is verified on benchmark datasets, i.e., UCLA, DynTex, and DynTex++, for DT classification issue. The experimental results substantiate the interest of our method.

对于计算机视觉中的不同任务，了解动态纹理（DT）的混沌运动是视频表示的一个难题。本文通过解决以下新颖概念，提出了一种有效的DT表示的新方法。首先，引入矩量模型作为一种有效的预处理技术，以较低的计算成本来丰富动态体素的鲁棒性和区分性信息。其次，提出了局部导数模式算子的两个重要扩展，以提高其捕获方向特征的性能。第三，考虑到过滤和基于局部特征的方法形成有效的DT描述符的优势，我们提出了一个新的框架，称为Momental Directional Patterns。此外，受卷积神经网络的激励，通过利用从最大合并视频中提取的更多全局特征来提高描述符的辨别能力，从而增强了所提出的框架。我们的建议已在DTLA分类问题的基准数据集（即UCLA，DynTex和DynTex ++）上得到验证。实验结果证实了我们方法的兴趣。