SMOOTH-INVARIANT GAUSSIAN FEATURES FOR DYNAMIC TEXTURE RECOGNITION

An efficient framework for dynamic texture (DT) representation is proposed by exploiting local features based on Local Binary Patterns (LBP) from filtered images. First, Gaussian smoothing filter is used to deal with near uniform regions and noise which are typical restrictions of LBP operator. Second, the receptive field of Difference of Gaussians (DoG), which is exploited in DT description for the first time, allows to make the descriptor more robust against the changes of environment, illumination, and scale which are main challenges in DT representation. Experimental results of DT recognition on different benchmark datasets (i.e., UCLA, DynTex, and DynTex++), which give outstanding performance compared to the state of the art, verify the interest of our proposal.