Global Contrast of Superpixels Based Salient Region Detection

  • Jie Wang
  • Caiming Zhang
  • Yuanfeng Zhou
  • Yu Wei
  • Yi Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7633)


Reliable estimation of visual saliency has become an essential tool in image processing. In this paper, we propose a novel salient region detection algorithm, superpixel contrast (SC), consisting of three basic steps. First, we decompose a given image into compact, regular superpixels that abstract unnecessary details by a new superpixel algorithm, hexagonal simple linear iterative clustering (HSLIC). Then we define the saliency of each perceptually meaningful superpixel instead of rigid pixel grid, simultaneously evaluating global contrast differences and spatial coherence. Finally, we locate the key region and enhance its saliency by a focusing step. The proposed algorithm is simple to implement and computationally efficient. Our algorithm consistently outperformed all state-of-the-art detection methods, yielding higher precision and better recall rates, when evaluated on well-known publicly available data sets.


saliency detection superpixel global contrast focusing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cong, L., Tong, R., Dong, J.: Selective image abstraction. Visual Comput. 27(3), 187–198 (2010)CrossRefGoogle Scholar
  2. 2.
    Fu, S., Zhang, C.: Adaptive bidirectional diffusion for image restoration. Sci. China Inform. Sci. 53(12), 2452–2460 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Fu, S., Zhang, C.: Image denoising and deblurring: non-convex regularization, inverse diffusion and shock filter. Sci. China Inform. Sci. 54, 1184–1198 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE T. Pattern Anal. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  5. 5.
    Harel, J., Koch, C.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems 19, pp. 545–552 (2007)Google Scholar
  6. 6.
    Achanta, R., Estrada, F.J., Wils, P., Süsstrunk, S.: Salient Region Detection and Segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Ma, Y., Zhang, H.: Contrast-based image attention analysis by using fuzzy growing. In: 11th ACM International Conference on Multimedia, pp. 374–381. ACM Press, New York (2003)Google Scholar
  8. 8.
    Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: 14th Annual ACM International Conference on Multimedia, pp. 815–824. ACM Press, New York (2006)CrossRefGoogle Scholar
  9. 9.
    Cheng, M., Zhang, G., Mitra, N., Huang, X., Hu, S.: Global contrast based salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition 2011, pp. 409–416. IEEE Press, New York (2011)Google Scholar
  10. 10.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition 2009, pp. 1597–1604. IEEE Press, New York (2009)CrossRefGoogle Scholar
  11. 11.
    Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition 2007, pp. 1–8. IEEE Press, New York (2007)CrossRefGoogle Scholar
  12. 12.
    Achanta, R., Susstrunk, S.: Saliency detection for content-aware image resizing. In: 16th IEEE International Conference on Image Processing, pp. 1005–1008. IEEE Press, New York (2009)Google Scholar
  13. 13.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  14. 14.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. Technical Report, 149300 EPFL (2010)Google Scholar
  15. 15.
    Hales, T.: The honeycomb conjecture. Discrete Comput. Geom. 25(1), 1–22 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Williams, D.: Topography of the foveal cone mosaic in the living human eye. Vision Res. 28(3), 433–454 (1988)CrossRefGoogle Scholar
  17. 17.
    Koffka, K.: Principles of Gestalt Psychology. Routledge and Kegan Paul (1955)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jie Wang
    • 1
  • Caiming Zhang
    • 1
    • 2
  • Yuanfeng Zhou
    • 1
  • Yu Wei
    • 1
  • Yi Liu
    • 1
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.Shandong Provincial Key Laboratory of Digital Media TechnologyShandong University of Finance and EconomicsJinanChina

Personalised recommendations