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Optimal Feature Selection for Saliency Seed Propagation in Low Contrast Images

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Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11166))

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Abstract

Salient object detection can substantially facilitate a wide range of applications. Although significant improvements have been made in recent years, low contrast image still pose great challenges to current methods due to its low signal to noise ratio property. In this paper, an optimal feature selection based saliency seed propagation method is presented to detect salient objects in low contrast images. The key idea of the proposed approach is to hierarchically refine the saliency map guided by adaptively selecting the optimal features in low contrast images recursively. Multiscale superpixel segmentation is firstly utilized to suppress background interference. Then, the initial saliency map can be generated via global contrast and spatial relationship. Local and global fitness are finally utilized to optimize the resulting saliency maps. Extensive experimental evaluations on four datasets demonstrate that the proposed model outperforms 15 state-of-the-art methods in terms of efficiency and accuracy.

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Notes

  1. 1.

    The nighttime image (NI) dataset of this paper can be downloaded from https://drive.google.com/open?id=0BwVQK2zsuAQwX2hXbnc3ZVMzejQ.

  2. 2.

    More detected saliency maps of various models on four datasets can be downloaded from https://drive.google.com/open?id=0BwVQK2zsuAQwSENvVVR1NUJzVGc.

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Acknowledgment

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

  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

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

Correspondence to Xin Xu .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

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Mu, N., Xu, X., Zhang, X. (2018). Optimal Feature Selection for Saliency Seed Propagation in Low Contrast Images. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-00764-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00763-8

  • Online ISBN: 978-3-030-00764-5

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