Advertisement

Similar Region Contrast Based Salient Object Detection

  • Qiang Fan
  • Chun Qi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7633)

Abstract

Detection of visual saliency is an important issue in many computer vision tasks. In this paper, we propose a novel regional contrast based saliency detection method, generating a saliency map that enables high contrast between the foreground salient object and background. Our method mainly integrates four principles, which are based on psychological evidences, visual research and general observation. In order to suppress the homogeneous regions, and let the novel regions stand out, our method computes a region’s saliency value based on the region’s N closest regions defined in the CIE L*a*b color space. We compared our method with the state-of-the-art saliency detection methods using a standard publicly available database. Experimental results show that our method has better performance on yielding higher precision and recall rates. In the application of image editing, we demonstrate that using our saliency map as energy map can achieve more appealing retargeting results with less distortions in the important regions.

Keywords

saliency detection high contrast closest regions 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Transactions on Graphics 26(3), 2462–2470 (2007)CrossRefGoogle Scholar
  2. 2.
    Han, J., Ngan, K.N., Li, M.J., Zhang, H.J.: Unsupervised extraction of visual attention objects in color images. IEEE Transactions on Circuits and Systems for Video Technology 16, 141–145 (2006)CrossRefGoogle Scholar
  3. 3.
    Rutishauser, U., Walther, D., Koch, C., Perona, P.: Is bottom-up attention useful for object recognition? In: IEEE CVPR, pp. 37–44 (2004)Google Scholar
  4. 4.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  5. 5.
    Koch, C., Poggio, T.: Predicting the visual world: silence is golden. Nature Neuroscience 2(1), 9–10 (1999)CrossRefGoogle Scholar
  6. 6.
    Treisman, A., Gelade, G.: Predicting the visual world: silence is golden. Cognitive Psychology 12(1), 97–136 (1980)CrossRefGoogle Scholar
  7. 7.
    Wolfe, J.M.: A revised model of visual search. Psychonomic Bulletin & Review 1(2), 202–238 (1994)CrossRefGoogle Scholar
  8. 8.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: ICCV, pp. 2106–2113 (2009)Google Scholar
  9. 9.
    Rother, Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. IJCV 59(2), 167–181 (2004)CrossRefGoogle Scholar
  10. 10.
    Cheng, M.M., Zhang, G.X., Mitra, N.J., Huang, X.L., Hu, S.M.: Global contrast based salient region detection. In: CVPR, pp. 409–416 (2011)Google Scholar
  11. 11.
    Gorisse, D., Cord, M., Precioso, F.: Locality-Sensitive Hashing for Chi2 Distance. IEEE TPAMI 34(2), 402–409 (2012)CrossRefGoogle Scholar
  12. 12.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. IJCV 40(2), 99–121 (2000)zbMATHCrossRefGoogle Scholar
  13. 13.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)Google Scholar
  14. 14.
    Hou, X.D., Zhang, L.Q.: Saliency Detection: A Spectral Residual Approach. In: CVPR, pp. 1–8 (2007)Google Scholar
  15. 15.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)Google Scholar
  16. 16.
    Achanta, R., Estrasda, F., Wils, P., Susstrunk, 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
  17. 17.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: CVPR, pp. 2376–2383 (2010)Google Scholar
  18. 18.
    Ma, Y.F., Zhang, H.J.: Contrast-based image attention analysis by using fuzzy growing. In: ACM MM, pp. 374–381 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qiang Fan
    • 1
  • Chun Qi
    • 1
  1. 1.School of Electronic and Information EngineeringXi’an Jiao Tong UniversityXianChina

Personalised recommendations