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Particle Swarm Optimization Based Salient Object Detection for Low Contrast Images

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10636))

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Abstract

Saliency detection has attracted increasing attentions in computer vision. Although most traditional saliency models can effectively detect the salient objects in natural images, it is still a burning problem in low contrast images, for low lightness and few color information limit the applicability of these models. Different from conventional models, which are not robust on weak light environments, the proposed method uses the particle swarm optimization (PSO) algorithm to estimate the image saliency. First, the covariance feature is used to compute the local saliency of each superpixel region. Then, the PSO search is executed to measure the image saliency in a global perspective. Finally, the graph model is constructed to optimize the saliency value. As the proposed model incorporates both local and global cues, the generated salient objects have well-defined boundaries and uniform inner regions. Experimental results show that the proposed salient object detection model yields better results than eleven state-of-the-art saliency models on low contrast images.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (61602349, 61440016, and 61273225), Hubei Chengguang Talented Youth Development Foundation (2015B22), and the Educational Research Project from the Educational Commission of Hubei Province (2016234).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Nan Mu, Xin Xu, Xiaolong Zhang & Li Chen

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, 430065, China

    Xin Xu, Xiaolong Zhang & Li Chen

Authors
  1. Nan Mu
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  2. Xin Xu
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  3. Xiaolong Zhang
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  4. Li Chen
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Corresponding author

Correspondence to Xin Xu .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Mu, N., Xu, X., Zhang, X., Chen, L. (2017). Particle Swarm Optimization Based Salient Object Detection for Low Contrast Images. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_61

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  • DOI: https://doi.org/10.1007/978-3-319-70090-8_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70089-2

  • Online ISBN: 978-3-319-70090-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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