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Exploiting Surroundedness and Superpixel cues for salient region detection

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Abstract

In this paper, we will present a new salient region detection method by exploiting its surrounding and superpixel cues. Its main highlights are: 1) An input image is quantized to 256 colors by using minimum variance quantization; 2) Saliency maps is computed based on the figure-ground segregation of the quantized image; 3) Mean saliency value of each superpixel is employed to refine saliency maps further. This can highlight salient objects robustly and suppress backgrounds evenly. Experimental results show that the proposed method produces more accurate saliency maps and performs well against twenty-one saliency models concerning three evaluation metrics on two public datasets.

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Notes

  1. The ASD data set is available at http://ivrl.epfl.ch/supplementary_material/RK_CVPR09/.

  2. The ImgSal data set is available at http://www.escience.cn/people/jianli/DataBase.html.

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Acknowledgments

This work was supported in part by Jiangsu Policy Guidance (Industry University Research) Project (Grant no. BY2018130), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (Grant No. U1501501), Industry-Academia Cooperation Innovation Fund Project of Jiangsu Province(BY2016030-06), Six Talent Peaks Project in Jiangsu Province (2016-XYDXXJS-020) and Changzhou Key Laboratory of Industrial Internet and Data Intelligence (No.CM20183002).

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Authors and Affiliations

  1. Information Center of Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China

    Yifeng Jiang

  2. Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China

    Shan Chang & Enxing Zheng

  3. Nanjing University of Science & Technology Zijin College, Nanjing, 210023, Jiangsu, China

    Linna Hu

  4. School of Automotive and Traffic Engineering, Jiangsu University of Technology, Changzhou, 213001, Jiangsu, China

    Ranran Liu

  5. Changzhou Key Laboratory of Industrial Internet and Data Intelligence, Changzhou Vocational Institute of Mechatronic Technology, Changzhou, 213164, China

    Ranran Liu

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  1. Yifeng Jiang
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  2. Shan Chang
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  3. Enxing Zheng
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  4. Linna Hu
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  5. Ranran Liu
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Correspondence to Ranran Liu.

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Jiang, Y., Chang, S., Zheng, E. et al. Exploiting Surroundedness and Superpixel cues for salient region detection. Multimed Tools Appl 79, 10935–10951 (2020). https://doi.org/10.1007/s11042-020-08783-z

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