Weighted Schatten p -norm and Laplacian scale mixture-based low-rank and sparse decomposition for foreground-background separation

Low-rank and sparse decomposition (LRSD) plays a vital role in foreground-background separation. The existing LRSD methods have the drawback: imprecise surrogate functions of rank and sparsity. We propose the weighted Schatten p-norm (WSN) and Laplacian scale mixture (LSM) method based on LRSD for foreground-background separation, which introduces the WSN and LSM to improve this drawback. To demonstrate the performance of the proposed method, it is applied to foreground-background separation and gets the highest average F-measure score.