Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model

• A multi-kernel based Gaussian process dynamic model is proposed for dynamic texture modeling.
• We design a two-step optimization algorithm to learn the multi-kernel based Gaussian process dynamic model.
• We design a dynamic texture synthesis algorithm based on mean prediction for the proposed multi-kernel based Gaussian process dynamic model.

Dynamic texture (DT) widely exists in various social video media. Therefore, DT modeling and synthesis plays an important role in social media analyzing and processing. In this paper, we propose a Bayesian-based nonlinear dynamic texture modeling method for dynamic texture synthesis. To capture the non-stationary distribution of DT, we utilize the Gaussian process latent variable model for dimensional reduction. Furthermore, we design a multi-kernel dynamic system for the latent dynamic behavior modeling. In our model, we do not make strong assumption on the nonlinear function. Instead, our model automatically constructs a suitable nonlinear kernel for dynamic modeling and therefore is capable of fitting various types of dynamics. We evaluate the effectiveness our methods on the DynTex database and compared with representative DT synthesis method. Experimental results show that our method can achieve synthesis results with higher visual quality.