目的 线性动态系统有效地捕捉了动态纹理在时间和空间的转移信息。然而，线性动态系统属于非欧氏空间模型，无法直接应用传统的稀疏编码进行分类识别，为此提出一种基于稀疏编码线性动态系统的求解方法并应用于动态纹理识别。方法 基于约束凸优化公式，将稀疏编码和控制论中相似性变换结合，优化学习模型参数，解决应用稀疏编码进行分类识别的问题，实现有效的动态纹理识别。结果 在公开的动态纹理图像数据库UCLA上进行实验并与其他方法进行比较，实验结果表明，本文方法具有更好的性能，识别率可达97%，且对遮挡具有更好的鲁棒性。结论 本文方法对动态纹理及遮挡情况具有更好的识别率。

Objective Linear dynamical system(LDS) as the description for dynamic texture can capture the transition of appearance and motion effectively. However, the LDS model does not belong to Euclidean space, making it impossible to apply traditional sparse coding techniques for classification and recognition. A novel approach based on sparse coding and LDS is proposed to be applied in dynamic texture recognition. Method The proposed algorithm employs a principled convex optimization formulation that allows both a sparse representation code and a linear transformation matrix to be jointly inferred. Model parameters are optimized and learned to realize good texture recognition. Result Experiments are conducted on publicly available dynamic texture databases UCLA, and comparison with other methods is made. Experimental results show that the proposed method has better performance, for the recognition rate 97% and better robustness to occlusion.Conclusion show that the proposed algorithm outperforms earlier approaches, including robustness to occlusion.