Completed statistical adaptive patterns on three orthogonal planes for recognition of dynamic textures and scenes
Thanh Tuan Nguyen, Thanh Phương Nguyễn, Frédéric Bouchara · Journal of Electronic Imaging · 2018
Dynamic texture (DT) and scene descriptions are challenging problems in video understanding that play crucial role in different important applications of computer vision. An efficient framework, called completed and statistical adaptive patterns on three orthogonal planes (CSAP-TOP), for representation of dynamic textures and scenes is addressed in this work. It utilizes adaptive thresholding applied in a completed schema on high-order moment images of three orthogonal planes. Some beneficial properties from its complementary features are inherited: taking into account information from local variations of magnitudes, robustness against noise and near-uniform regions, exploiting more useful and stable textural information at local and regional scales. In addition, we also consider the impact of high-order filtered images and adaptive thresholding in volume statistical adaptive patterns (VSAP) to investigate their influence and efficiency on describing DT and scene sequences. Evaluations of recognition on different datasets of dynamic textures and scenes (UCLA, DynTex, YUPENN, YUP++) show that our proposal, without utilizing sophisticated learning techniques, significantly outperforms recent results in the state-of-the-art.