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Salient object detection via robust dictionary representation
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DOI：10.1007/s11042-017-5118-7.png)
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Abstract
En 中文
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 a (2,1)-norm. Correspondingly, Frobenius norm instead of a (1)-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.
Keywords:
Salient object detection
Low-rank representation
Sparse representation
Matrix decomposition
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