The Visual Computer

, Volume 31, Issue 3, pp 355–364 | Cite as

Saliency detection with color contrast based on boundary information and neighbors

  • Min XuEmail author
  • Hanling Zhang
Original Article


Object-level saliency detection is significant in many computer vision tasks. In this paper, we propose a novel saliency detection model based on color contrast and image boundaries. The saliency of an image is defined as the contrast between the image elements (regions) and image boundaries elements (regions). We consider the saliency in two-stage procedure rather than in one stage. First of all, according to the definition of saliency, we take four boundaries of image into consideration respectively to obtain a combination coarse saliency map. Furthermore, a new energy function based on the coarse saliency map is proposed, which takes the coarse saliency map as input to yield the final full resolution saliency map. Experimental results on two public datasets demonstrate that the proposed model performs better than the state-of-the-art methods.


Visual saliency Color contrast Energy function  Saliency map 



This work was supported by the Key Project of Hunan Province Science and Technology Planning Project, China (2014G2012).


  1. 1.
    Yarbus, A.: Eye movements and vision. Plenum Press, New York (1967)CrossRefGoogle Scholar
  2. 2.
    Neisser, U.: Cognitive psychology. Appleton-Century-Crofts (1967)Google Scholar
  3. 3.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  4. 4.
    Tsotsos, J.: What roles can attention play in recognition? In: Proceedings of seventh IEEE international conference development and learning, pp. 55–60 (2008)Google Scholar
  5. 5.
    Privitera, C.M., Stark, L.W.: Algorithms for defining visual regionsof-interest: comparison with eye fixations. IEEE Trans. Pattern Anal. Mach. Intell. 22(9), 970–982 (2000)CrossRefGoogle Scholar
  6. 6.
    Siagian, C., Itti, L.: Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 300–312 (2007)CrossRefGoogle Scholar
  7. 7.
    Gao, D., Han, S., Vasconcelos, N.: Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 989–1005 (2009)CrossRefGoogle Scholar
  8. 8.
    Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. Image Process. 13(10), 1304–1318 (2004)CrossRefGoogle Scholar
  10. 10.
    Achanta, R., Estrada, F., Wils, P., et al.: Salient region detection and segmentation. In ICVS (2008)Google Scholar
  11. 11.
    Achanta, R., Hemami, S., Estrada, F., et al.: Frequency-tuned salient region detection. In CVPR (2009)Google Scholar
  12. 12.
    Bruce, N., Tsotsos, J.: Saliency based on information maximization. In NIPS (2005)Google Scholar
  13. 13.
    Cheng, M.M., Zhang, G.X., Mitra, N.J., et al.: Global contrast based salient region detection. In CVPR (2011)Google Scholar
  14. 14.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection, In CVPR (2010)Google Scholar
  15. 15.
    Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs for salient object detection in images. IEEE TIP (2010)Google Scholar
  16. 16.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In NIPS (2006)Google Scholar
  17. 17.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In CVPR (2007)Google Scholar
  18. 18.
    Lu, Y., Zhang, W., Lu, H., et al.: Salient object detection using concavity context. In ICCV (2011)Google Scholar
  19. 19.
    Wang, L., Xue, J., Zheng, N., et al.: Automatic salient object extraction with contextual cue. In ICCV (2011)Google Scholar
  20. 20.
    Ma, Y., Zhang, H.: Contrast-based image attention analysis by using fuzzy growing. ACM Multimedia (2003)Google Scholar
  21. 21.
    Perazzi, F., Krahenbuhl, P., Pritch, Y., et al.: Saliency filters: contrast based filtering for salient region detection. In CVPR (2012)Google Scholar
  22. 22.
    Wang, W., Wang, Y., Huang, Q., et al.: Measuring visual saliency by site entropy rate. In CVPR (2010)Google Scholar
  23. 23.
    Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. ACM Multimedia (2006)Google Scholar
  24. 24.
    Liu, T., Yuan, Z., Sun, J., et al.: Learning to detect a salient object. IEEE PAMI (2011)Google Scholar
  25. 25.
    Yang, J., Yang, M.: Top-down visual saliency via joint crf and dictionary learning. In CVPR (2012)Google Scholar
  26. 26.
    Li, J., Levine, M.D., An, X., et al.: Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2013)Google Scholar
  27. 27.
    Xie, Y.L., Lu, H.C., Yang, M.H.: Bayesian saliency via low and mid level cues. IEEE TIP 1, 6 (2013)Google Scholar
  28. 28.
    Wei, Y.C., Wen, F., Zhu, W.J., et al.: Geodesic saliency using background priors. In ECCV 2, 6 (2012)Google Scholar
  29. 29.
    Achanta, R., Smith, K., Lucchi, A., et al.: Slic super-pixels. Technical report, EPFL, Tech. Rep. 149300 (2010)Google Scholar
  30. 30.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)CrossRefGoogle Scholar
  31. 31.
    Tatler, B.: The central fixation bias in scene viewing: selecting an optimal viewing position ndependently of motor biases and image feature distributions. J. Vis. 7(14), 1–17 (2007)CrossRefGoogle Scholar
  32. 32.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cueintegration, In CVPR (2007)Google Scholar
  33. 33.
    Jiang, H., Wang, J., Yuan, Z., et al.: Automatic salient object segmentation based on context and shapeprior. In BMVC (2011)Google Scholar
  34. 34.
    Li, J., Tian, Y., Duan, L., et al.: Estimating Visual saliency through single image optimization. IEEE Signal Process. Lett. 20 (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Information Science and EngineeringHunan UniversityChangshaChina

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