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Salient object detection via robust dictionary representation

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Abstract

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 ℓ 2,1-norm. Correspondingly, Frobenius norm instead of ℓ 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.

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Notes

  1. “sample-specific” corruptions mean that some data samples are corrupted and the others are clean [23].

  2. When D is designed to be a full rank matrix, then rank(DZ) = rank(Z). Since F ≈DZ, then rank(D) ≈ rank(Z). So, if F is low-rank, we can obtain that Z is low-rank.

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Acknowledgments

This research was partially supported by National Natural Science Foundation of China under project No. 61403403, No. 71673293 and China Postdoctoral Science Foundation under project No. 2015M52707.

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Authors and Affiliations

  1. Department of System Engineering, National University of Defense Technology, Changsha, China

    Huaxin Xiao, Yu Liu & Maojun Zhang

  2. Department of Management Science and Engineering, Officers College of Chinese Armed Police Force, Chengdu, China

    Weiya Ren

  3. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

    Wei Wang

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  1. Huaxin Xiao
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  2. Weiya Ren
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  3. Wei Wang
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  4. Yu Liu
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  5. Maojun Zhang
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Correspondence to Huaxin Xiao.

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Huaxin Xiao and Weiya Ren has an equal contribution.

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Xiao, H., Ren, W., Wang, W. et al. Salient object detection via robust dictionary representation. Multimed Tools Appl 77, 3317–3337 (2018). https://doi.org/10.1007/s11042-017-5118-7

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