Saliency Detection via Diversity-Induced Multi-view Matrix Decomposition

In this paper, a diversity-induced multi-view matrix decomposition model (DMMD) for salient object detection is proposed. In order to make the background cleaner, \(\mathrm {Schatten}\)-p norm with an appropriate value of p in (0,1] is used to constrain the background part. A group sparsity induced norm is imposed on the foreground (salient part) to describe potential spatial relationships of patches. And most importantly, a diversity-induced multi-view regularization based Hilbert-Schmidt Independence Criterion (HSIC), is employed to explore the complementary information of different features. The independence between the multiple features will be enhanced. The optimization problem can be solved through an augmented Lagrange multipliers method. Finally, high-level priors are merged to boom the salient regions detection. Experiments on the widely used MSRA-5000 dataset show that the DMMD model outperforms other state-of-the-art methods.