LAUSR

The theory of sparse and low-rank representation has worked competitive performance in the field of salient object detection. Generally, the salient object is represented as sparse error while the non-salient region is constrained by the property of low-rank. However, sparsity ignores the global structure which may break up the low-rank property. Besides, the outliers always lead to a poor representation. To handle these problems, this paper proposes a robust representation based on a discriminative dictionary which consists of non-salient and salient templates. Three weight measures are introduced and combined to select the proper templates. The coefficients on dictionary are restricted by ℓ2,1-norm. Correspondingly, Frobenius norm instead of ℓ1-norm is exploited to constrain the distribution of representation error. We compare the proposed algorithm against 17 state-of-the-art methods on 4 popular datasets by 6 evaluation metrics and demonstrate the competitive performance in terms of qualitative and quantitative results.