Saliency Detection Based on the Integration of Global Contrast and Superpixels

  • Yikun HuangEmail author
  • Lu Liu
  • Yan Li
  • Jie Chen
  • Jiawei Lu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 157)


In the field of computer vision, the detection of salient object is an important step and one of the preconditions for salient object extraction. The outcome resulting from some existing detection methods for salient object is considerably different from the Ground Truth. In view of the shortcomings of existing methods, this paper proposes a saliency detection method based on the integration of global contrast and superpixels. The salience value of each pixel is measured according to the global contrast of the pixels in the image. A histogram optimization technique is used to highlight the low-contrast pixels of the salient region in the image and omit the high-contrast pixels of the background. In order to improve the image quality of the salient image, the superpixel image segmentation based on K-Means clustering algorithm is proposed, and finally, we generate a more accurate saliency map through the integration with superpixels. The experiment is performed on the public dataset MSRA10 K. The results show that the histogram optimization can help improve the contrast of the salient pixels and generate a better saliency map by integrating with superpixels. Compared with other classical algorithms, the proposed method outperforms other methods.


Global contrast Histogram Superpixels Saliency detection 



This work is supported by the 2018 Program for Outstanding Young Scientific Researcher in Fujian Province University, Education and Scientific Research Project for Middle-aged and Young Teachers in Fujian Province (No: JZ170367).


  1. 1.
    Karczmarek, P., et al.: A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft. Comput. 21(24), 7503–7517 (2017)CrossRefGoogle Scholar
  2. 2.
    Hui, B., et al.: Accurate image segmentation using Gaussian mixture model with saliency map. Pattern Anal. Appl. 2, 1–10 (2018)Google Scholar
  3. 3.
    Yikun, Huang: Simulation of parallel fusion method for multi-feature in double channel video image. Comput. Simul. 35(4), 154–157 (2018)Google Scholar
  4. 4.
    Niu, Y., Chen, J., Guo, W.: Meta-metric for saliency detection evaluation metrics based on application preference. Multimed. Tools Appl. 4, 1–19 (2018)Google Scholar
  5. 5.
    Xue, X., Wang, Y.: Using memetic algorithm for instance coreference resolution. IEEE Trans. Knowl. Data Eng. 28(2), 580–591 (2016)CrossRefGoogle Scholar
  6. 6.
    Yun, Z., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: ACM International Conference on Multimedia (2006)Google Scholar
  7. 7.
    Cheng, M.M., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)CrossRefGoogle Scholar
  8. 8.
    Niu, Y., Lin, W., Ke, X.: CF-based optimisation for saliency detection. IET Comput. Vis. 12(4), 365–376 (2018)CrossRefGoogle Scholar
  9. 9.
    Ishikura, K., et al.: Saliency detection based on multiscale extrema of local perceptual color differences. IEEE Trans. Image Process. 27(2), 703 (2018)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Singh, A., Yadav, S., Singh, N.: Contrast enhancement and brightness preservation using global-local image enhancement techniques. In: Fourth International Conference on Parallel (2017)Google Scholar
  11. 11.
    Cuevas-Olvera, M., et al.: Salient Object Detection in Digital Images Based on Superpixels and Intrinsic Features. IEEE (2018)Google Scholar
  12. 12.
    Liu, S., Jiang, N., Liu, Z.: Saliency detection of infrared image based on region covariance and global feature. J. Syst. Eng. Electron. 29(3), 483–490 (2018)Google Scholar
  13. 13.
    Niu, Y., Su, C., Guo, W.: Salient object segmentation based on superpixel and background connectivity prior. IEEE Access 6, 56170–56183 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yikun Huang
    • 1
    Email author
  • Lu Liu
    • 1
  • Yan Li
    • 2
  • Jie Chen
    • 3
  • Jiawei Lu
    • 3
  1. 1.Concord University CollegeFujian Normal UniversityFuzhouChina
  2. 2.Minnan University of Science and TechnologyQuanzhouChina
  3. 3.Intelligent Information Processing Research Center, College of Information Science and Engineering, Fujian University of TechnologyFuzhouChina

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