A Visual Saliency Map Based on Random Sub-window Means

  • Tadmeri Narayan Vikram
  • Marko Tscherepanow
  • Britta Wrede
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

In this article, we propose a simple and efficient method for computing an image saliency map, which performs well on both salient region detection and as well as eye gaze prediction tasks. A large number of distinct sub-windows with random co-ordinates and scales are generated over an image. The saliency descriptor of a pixel within a random sub-window is given by the absolute difference of its intensity value to the mean intensity of the sub-window. The final saliency value of a given pixel is obtained as the sum of all saliency descriptors corresponding to this pixel. Any given pixel can be included by one or more random sub-windows. The recall-precision performance of the proposed saliency map is comparable to other existing saliency maps for the task of salient region detection. It also achieves state-of-the-art performance for the task of eye gaze prediction in terms of receiver operating characteristics.

Keywords

Bottom-up Visual Attention Saliency Map Salient Region Detection Eye Fixation 

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References

  1. 1.
    Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. Pattern Analysis Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  2. 2.
    Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Processing 19(1), 185–198 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Rothenstein, A.L., Tsotsos, J.K.: Attention links sensing to recognition. Image and Vision Computing 26(1), 114–126 (2008)CrossRefGoogle Scholar
  4. 4.
    Elazary, L., Itti, L.: A Bayesian model for efficient visual search and recognition. Vision Research 50(14), 1338–1352 (2010)CrossRefGoogle Scholar
  5. 5.
    Moosmann, F., Larlus, D., Jurie, F.: Learning Saliency Maps for Object Categorization. In: ECCV International Workshop on The Representation and Use of Prior Knowledge in Vision (2006)Google Scholar
  6. 6.
    Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Frequency tuned Salient Region Detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  7. 7.
    Buschman, T.J., Miller, E.K.: Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices. Science 315(5820), 1860–1862 (2007)CrossRefGoogle Scholar
  8. 8.
    Seo, H.J., Milanfar, P.: Static and Space-time Visual Saliency Detection by Self- Resemblance. Journal of Vision 9(12), 1–27 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: A Bayesian Framework for Saliency Using Natural Statistics. Journal of Vision 8(7), 1–20 (2008)CrossRefGoogle Scholar
  10. 10.
    Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Processing 19(1), 185–198 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Cui, X., Liu, Q., Metaxas, D.: Temporal spectral residual: fast motion saliency detection. In: ACM International Conference on Multimedia, pp. 617–620 (2009)Google Scholar
  12. 12.
    Rosin, P.L.: A simple method for detecting salient regions. Pattern Recognition 42(11), 2363–2371 (2009)CrossRefMATHGoogle Scholar
  13. 13.
    Achanta, R., Süsstrunk, S.: Saliency Detection using Maximum Symmetric Surround. In: IEEE International Conference on Image Processing (2010)Google Scholar
  14. 14.
    Vikram, T.N., Tscherepanow, M., Wrede, B.: A Random Center Surround Bottom up Visual Attention Model useful for Salient Region Detection. In: IEEE Workshop on Applications of Computer Vision, pp. 166–173 (2011)Google Scholar
  15. 15.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Neural Information Processing Systems, pp. 545–552 (2007)Google Scholar
  16. 16.
    Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to Detect A Salient Object. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  17. 17.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE International Conference on Computer Vision (2009)Google Scholar
  18. 18.
    Bruce, N.D., Tsotsos, J.K.: Attention based on Information Maximization. In: The International Conference on Computer Vision Systems (2007)Google Scholar
  19. 19.
    Mante, V., Frazor, R.A., Bonin, V., Geisler, W.S., Carandini, M.: Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neuroscience 8(12), 1690–1697 (2005)CrossRefGoogle Scholar
  20. 20.
    Soltani, A., Koch, C.: Visual Saliency Computations: Mechanisms, Constraints, and the Effect of Feedback. Neuroscience 30(38), 12831–12843 (2010)CrossRefGoogle Scholar
  21. 21.
    Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology 4(4), 219–227 (1985)Google Scholar
  22. 22.
    Gao, D., Mahadevan, V., Vasconcelos, N.: The discriminant center-surround hypothesis for bottom up saliency. In: Neural Information Processing Systems (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tadmeri Narayan Vikram
    • 1
    • 2
  • Marko Tscherepanow
    • 1
  • Britta Wrede
    • 1
    • 2
  1. 1.Applied Informatics GroupBielefeld UniversityBielefeldGermany
  2. 2.Research Institute for Cognition and Robotics (CoR-Lab)Bielefeld UniversityBielefeldGermany

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