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Visual Saliency Based on Two-Dimensional Fractional Fourier Transform

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Big Data (BigData 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1120))

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Abstract

Visual saliency is very helpful for image detection and image processing. This paper proposes a novel visual saliency model. First, the proposed model can extract a saliency map with high precision and compound the linear combination of saliency map. Second, based on two-dimensional fractional Fourier transform, the proposed model generates a robust saliency map from the input image with Gaussian or salt-and-pepper noise. In order to reveal the noise influence from the given image, we provide a concept called the noise sensitivity scale (NSS). Third, using the image database from MSRA10K, we analyze the precision-recall and ROC curve and experimentally demonstrate that the proposed model can evaluate human fixation to some extent.

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Author information

Authors and Affiliations

  1. School of Robot Engineering, Yangtze Normal University, Chongqing, People’s Republic of China

    Haibo Xu

  2. College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing, People’s Republic of China

    Chengshun Jiang

Authors
  1. Haibo Xu
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  2. Chengshun Jiang
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Corresponding author

Correspondence to Chengshun Jiang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Hai Jin

  2. East China Normal University, Shanghai, China

    Xuemin Lin

  3. Chinese Academy of Sciences, Beijing, China

    Xueqi Cheng

  4. Huazhong University of Science and Technology, Wuhan, China

    Xuanhua Shi

  5. National University of Defense Technology, Changsha, China

    Nong Xiao

  6. Nanjing University, Nanjing, China

    Yihua Huang

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The authors declare that there are no conflicts of interest regarding the publication of this article.

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© 2019 Springer Nature Singapore Pte Ltd.

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Xu, H., Jiang, C. (2019). Visual Saliency Based on Two-Dimensional Fractional Fourier Transform. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_17

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  • DOI: https://doi.org/10.1007/978-981-15-1899-7_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1898-0

  • Online ISBN: 978-981-15-1899-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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