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 synthesis model for high-dimensional DT from a small number of training samples. In this article, we propose a novel DT synthesis method, which makes full use of similarity as prior knowledge to address this issue. Our method is based on the proposed kernel similarity embedding, which can not only mitigate the high dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationships. Specifically, we first put forward two hypotheses that are essential for the DT model to generate new frames using similarity correlations. Then, we integrate kernel learning and the extreme learning machine into a unified synthesis model to learn kernel similarity embeddings for representing DTs. 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 embeddings can provide discriminative representations for DTs. Further, our method can preserve the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with higher speed and lower computation than the current state-of-the-art methods. The code and more synthesis videos are available at our project pagehttps://shiming-chen.github.io/Similarity-page/Similarit.html.

It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this paper, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate that the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. The experimental results demonstrate the superiority of our proposed approach when compared with the other state-of-the-art trackers.

Single image super-resolution (SR) has gained popularity to construct a high-resolution (HR) image from a single low-resolution (LR) version. More recently, non-local self similarity (NSS) has been attracted enormous interests in the field of SR, and the non-local means (NLM)-based methods are classical NSS-based SR methods. However, NLM-based methods neglect the structure information in the patches and structural similarity between patches, so it will be prone to introduce unexpected details into resultant HR images. In this paper, we propose a non-local balanced low rank matrix restoration model (NB-LRM) to improve the performance of SR which will overcome the drawbacks of NLM-based methods and take full advantage of the NSS prior. The proposed algorithm formulates the constrained optimization problem for HR image recovery. First, to take advantage of the local structure in the patch and the structural similarity between the non-local similar patches, we propose a measurement of the similarity based on both Euclidean distance and Pearson distance, then reconstruct the target patch by weighted average the similar patches. Second, to guarantee the structural similarity and linear correlation between the target patch and similar patches, we propose a new low rank regular term. Third, we introduce the iterative low rank regular algorithm to solve our model. Addition, this method doesn't need other image priors and can produce more robust reconstruction of image local structures. Compared with state-of-the-art SR methods, the proposed NB-LRM method achieves highly competitive PSNR and SSIM result, while demonstrating better edge and texture preservation performance.