A sequential subspace learning method and its application to dynamic texture analysis

Incremental update of subspace has new and interesting research applications in vision such as active recognition, object tracking and dynamic texture analysis. In this paper, a sequential subspace learning method is proposed for dynamic texture analysis. The learning algorithm can update adaptively dynamic texture subspace based on sequential observation data, and has higher computation efficiency and numerical stableness. Also our learning method considers the change of the texture sample mean when each new observation datum arrives, whereas existing subspace learning methods ignore the fact that the sample mean varies over time. Experimental results show the learning method for dynamic texture subspace is efficient and effective.