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. 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. 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. show that the proposed algorithm outperforms earlier approaches

including robustness to occlusion.