On-line learning parts-based representation via incremental orthogonal projective non-negative matrix factorization

This paper presents a novel incremental orthogonal projective non-negative matrix factorization (IOPNMF) algorithm, which is aimed to learn a parts-based subspace that reveals dynamic data streams. By assuming that the newly added samples only affect basis vectors but do not affect the coefficients of old samples, we propose an objective function for on-line learning and then present a multiplicative update rule to solve it. Compared with other non-negative matrix factorization (NMF) methods, our algorithm can guarantee to learn a linear parts-based subspace in an on-line fashion, which may facilitate some real applications. The facial analysis experiment shows that our IOPNMF method learns parts-based components successfully. In addition, we present an effective tracking method by integrating the IOPNMF method, the idea of sparse representation and the domain information of object tracking. The proposed tracker explicitly takes partial occlusion and mis-alignment into account for appearance model update and object tracking. The experimental results on some challenging image sequences demonstrate the proposed tracking algorithm performs favorably against several state-of-the-art methods.

► We present an incremental orthogonal projective NMF method. ► The IOPNMF method can learn parts-based representation in an on-line fashion. ► Facial experiments show that IOPNMF learns parts-based components successfully. ► We apply the IOPNMF method to object tracking. ► We propose a robust tracker by using IOPNMF and L1 regularization.