Implicit Rank-Sparsity Decomposition: Applications to Saliency/Co-saliency Detection

Modern techniques rely on convex relaxation to derive tractable approximations for rank-sparsity decomposition. However, the resultant precision loss usually deteriorates the performance in real-world applications. In this paper, we focus on the topic of visual saliency detection and consider the inherent uncertainty existing in observations, which may originate from both low-rank and sparse components. We formulate the rank-sparsity model with an implicit weighting factor and show that this weighting factor characterizes the nature of visual saliency. The proposed model is generalized to solve saliency and co-saliency detection in a unified way. In addition, this model can easily incorporate center-prior or other top-down priors and can extend to multi-task learning to explore the interrelation between multiple features. Experimental results demonstrate that our method improves existing rank-sparsity decomposition, and also outperforms most state of the arts on two salient object databases.