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Integrating Multiscale Contrast Regions for Saliency Detection

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Abstract

Visual saliency detection has lately witnessed substantial progress attributed to powerful feature representation leveraging deep convolutional neural networks (CNNs). However, existing CNN-based method has a lot of redundant computation resulting in inferring saliency maps is very time-consuming. In this paper, we propose a multiscale contrast regions deep learning framework employed to calculate salient score of an integrated image. Experimental results demonstrate that our approach is capable of achieving almost the same performance on the four public benchmarks compared to the relevant method MDF. Meanwhile, the computational efficiency is remarkably improved, when inferring the image of 400 * 300 size only takes average 3.32 s using our algorithm while MDF method consumes 8.0 s reducing rough 60% cost.

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Authors and Affiliations

  1. Guangdong University of Technology, Panyu District, Guangzhou, China

    Taizhe Tan, Qunsheng Zeng & Kangxi Xuan

  2. Synergy Innovation Institute of GDUT, Heyuan, China

    Taizhe Tan & Qunsheng Zeng

Authors
  1. Taizhe Tan
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  2. Qunsheng Zeng
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  3. Kangxi Xuan
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Corresponding author

Correspondence to Qunsheng Zeng .

Editor information

Editors and Affiliations

  1. Southeast University, Nanjing, China

    Xin Geng

  2. University of Tasmania, Hobart, Tasmania, Australia

    Byeong-Ho Kang

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Tan, T., Zeng, Q., Xuan, K. (2018). Integrating Multiscale Contrast Regions for Saliency Detection. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-97310-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97309-8

  • Online ISBN: 978-3-319-97310-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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