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Saliency Detection via A Graph Based Diffusion Model

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Graph-Based Representations in Pattern Recognition (GbRPR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10310))

Abstract

This paper proposes a graph based diffusion method for image saliency detection problem by adopting random walk with restart (RWR) model. Our method begins with computing background and foreground priors respectively for the input image. Based on these priors, we then adopt RWR method to obtain more reasonable and accurate background and foreground measurements by further considering the local structure of image. At last, we combine both background and foreground measurements together to obtain a more accurate saliency estimation. Experimental evaluations on four benchmark datasets demonstrate the benefits and effectiveness of the proposed method.

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Acknowledgement

This study was funded by the National Key Basic Research Program of China (973 Program) (2015CB351705); National Nature Science Foundation of China (61602001, 61572030, 61671018); Natural Science Foundation of Anhui Province (1708085QF139); Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2016A020).

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Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230601, China

    Zhouqin He, Bo Jiang, Yun Xiao, Chris Ding & Bin Luo

  2. CSE Department, University of Texas at Arlington, Arlington, TX, 76019, USA

    Chris Ding

Authors
  1. Zhouqin He
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  2. Bo Jiang
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  3. Yun Xiao
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  4. Chris Ding
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  5. Bin Luo
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Corresponding author

Correspondence to Bo Jiang .

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Editors and Affiliations

  1. Università degli Studi di Salerno, Fisciano, Italy

    Pasquale Foggia

  2. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Università degli Studi di Salerno, Fisciano, Italy

    Mario Vento

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He, Z., Jiang, B., Xiao, Y., Ding, C., Luo, B. (2017). Saliency Detection via A Graph Based Diffusion Model. In: Foggia, P., Liu, CL., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2017. Lecture Notes in Computer Science(), vol 10310. Springer, Cham. https://doi.org/10.1007/978-3-319-58961-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-58961-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58960-2

  • Online ISBN: 978-3-319-58961-9

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