Sparse Coding of Linear Dynamical Systems with an Application to Dynamic Texture Recognition

Sparse Coding of Linear Dynamical Systems with an Application to Dynamic Texture Recognition

Bernard Ghanem, Narendra Ahuja
"Sparse Coding of Linear Dynamical Systems with an Application to Dynamic Texture Recognition"
International Conference on Pattern Recognition (ICPR 2010)​
Bernard Ghanem and Narendra Ahuja
Texture Recognition, Sparse Optimization
2010
​Given a sequence of observable features of a linear dynamical system (LDS), we propose the problem of finding a representation of the LDS which is sparse in terms of a given dictionary of LDSs. Since LDSs do not belong to Euclidean space, traditional sparse coding techniques do not apply. We propose a probabilistic framework and an efficient MAP algorithm to learn this sparse code. Since dynamic textures (DTs) can be modeled as LDSs, we validate our framework and algorithm by applying them to the problems of DT representation and DT recognition. In the case of occlusion, we show that this sparse coding scheme outperforms conventional DT recognition methods.