  Dynamic texture (DT) exhibits statistical stationarity in the spatial domain
and stochastic repetitiveness in the temporal dimension, indicating that
different frames of DT possess a high similarity correlation that is critical
prior knowledge. However, existing methods cannot effectively learn a promising
synthesis model for high-dimensional DT from a small number of training data.
In this paper, we propose a novel DT synthesis method, which makes full use of
similarity prior knowledge to address this issue. Our method bases on the
proposed kernel similarity embedding, which not only can mitigate the
high-dimensionality and small sample issues, but also has the advantage of
modeling nonlinear feature relationship. Specifically, we first raise two
hypotheses that are essential for DT model to generate new frames using
similarity correlation. Then, we integrate kernel learning and extreme learning
machine into a unified synthesis model to learn kernel similarity embedding for
representing DT. Extensive experiments on DT videos collected from the internet
and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex,
demonstrate that the learned kernel similarity embedding can effectively
exhibit the discriminative representation for DT. Accordingly, our method is
capable of preserving the long-term temporal continuity of the synthesized DT
sequences with excellent sustainability and generalization. Meanwhile, it
effectively generates realistic DT videos with fast speed and low computation,
compared with the state-of-the-art methods. The code and more synthesis videos
are available at our project page
https://shiming-chen.github.io/Similarity-page/Similarit.html.
