Momental directional patterns for dynamic texture recognition

Thanh Tuan Nguyen, Thanh Phuong Nguyen, Frédéric Bouchara, Xuan Son Nguyen
2019 Computer Vision and Image Understanding  
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
more » ... vative 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 1 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.
doi:10.1016/j.cviu.2019.102882 fatcat:y57ug4ctlfh7hgq7ammy77yx6a