Rubik Gaussian-based patterns for dynamic texture classification

Thanh Tuan Nguyen (1) , Thanh Phuong Nguyen (1) , Frédéric Bouchara (2)

1 SIIM - Signal et Image
2 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)

Illumination, noise, and changes of environments, scales negatively impact on encoding chaotic motions for dynamic texture (DT) representation. This paper proposes a new method to overcome those issues by addressing the following novel concepts. First, different Gaussian-based kernels are taken into account as an effective filtered pre-processing with low computational cost to point out robust and invariant features. Second, a discriminative operator, named Local Rubik-based Pattern (LRP), is introduced to adequately capture both shape and motion cues of DTs by proposing a new concept of complemented components together with an effective encoding method. In addition, it also addresses a novel thresholding to take into account rich spatio-temporal relationships extracted from a new model of neighborhood supporting region. Finally, an efficient framework for DT description is presented by exploiting operator LRP for encoding various instances of Gaussian-based volumes in order to form a robust descriptor against noise, changes of illumination, scale, and environment. Experiments for DT classification on benchmark datasets have authenticated the interest of our proposal.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Mathématique discrète [cs.DM]

Thanh Tuan Nguyen, Thanh Phuong Nguyen, Frédéric Bouchara. Rubik Gaussian-based patterns for dynamic texture classification. Pattern Recognition Letters, In press, ⟨10.1016/j.patrec.2020.04.007⟩. ⟨hal-02535942⟩