Advertisement

Bottom-Up Saliency Detection Model Based on Amplitude Spectrum

  • Yuming Fang
  • Weisi Lin
  • Bu-Sung Lee
  • Chiew Tong Lau
  • Chia-Wen Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)

Abstract

In this paper, we propose a saliency detection model based on amplitude spectrum. The proposed model first divides the input image into small patches, and then uses the amplitude spectrum of the Quaternion Fourier Transform (QFT) to represent the color, intensity and orientation distributions for each patch. The saliency for each patch is determined by two factors: the difference between amplitude spectrums of the patch and its neighbor patches and the Euclidian distance of the associated patches. The novel saliency measure for image patches by using amplitude spectrum of QFT proves promising, as the experiment results show that this saliency detection model performs better than the relevant existing models.

Keywords

Quanternion Fourier Transform Amplitude Spectrum Visual Attention Saliency Detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69, 529–541 (1981)CrossRefGoogle Scholar
  2. 2.
    Piotrowski, L.N., Campbell, F.W.: A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception 11, 337–346 (1982)CrossRefGoogle Scholar
  3. 3.
    Hou, X., Zhang, L.: Saliency Detection: A spectral residual approach. In: Proceedings of IEEE CVPR (2007)Google Scholar
  4. 4.
    Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of IEEE CVPR (2008)Google Scholar
  5. 5.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelop. International Journal of Computer Vision 42, 145–175 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Treisman, A., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)CrossRefGoogle Scholar
  7. 7.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  8. 8.
    Bruce, N.D., Tsotsos, J.K.: Saliency based on information maximization. Advances in Neural Information Processing Systems 18, 155–162 (2006)Google Scholar
  9. 9.
    Brecht, M.D., Saiki, J.: A neural network implementation of a saliency map model. Neural Networks 19, 1467–1474 (2006)CrossRefzbMATHGoogle Scholar
  10. 10.
    Lu, Z., Lin, W., Yang, X., Ong, E., Yao, S.: Modeling visual attention’s modulatory aftereffects on visual sensitivity and quality evaluation. IEEE Transactions on Image Processing 14(11), 1928–1942 (2005)CrossRefGoogle Scholar
  11. 11.
    Ell, T., Sangwin, S.: Hypercomplex Fourier Transforms of Color Images. IEEE Transactions on Image Processing 16(1), 22–35 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Engel, S., Zhang, X., Wandell, B.: Colour Tuning in Human Visual Cortex Measured With Functional Magnetic Resonance Imaging. Nature 388(6), 68–71 (1997)CrossRefGoogle Scholar
  13. 13.
    Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 28(5), 802–817 (2006)CrossRefGoogle Scholar
  14. 14.
    Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: Proc. CVPR (2007)Google Scholar
  15. 15.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Visual Research 40, 1489–1506 (2000)Google Scholar
  16. 16.
    Gao, D., Vasconcelos, N.: Bottom-up saliency is a discriminant process. In: Proceedings of IEEE CVPR (2007)Google Scholar
  17. 17.
    Tatler, B.W., Baddeley, R.J., Gilchrist, I.D.: Visual correlates of fixation selection: effects of scale and time. Visual Search 45, 643–659 (2005)Google Scholar
  18. 18.
    Gopalakrishnan, V., Hu, Y., Rajan, D.: Salient region detection by modeling distributions of color and orientation. IEEE Transactions on Multimedia 11(5), 892–905 (2009)CrossRefGoogle Scholar
  19. 19.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Englewood CliffsGoogle Scholar
  20. 20.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proc. NIPS (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yuming Fang
    • 1
  • Weisi Lin
    • 1
  • Bu-Sung Lee
    • 1
  • Chiew Tong Lau
    • 1
  • Chia-Wen Lin
    • 2
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Department of Electrical EngineeringNational Tsing Hua UniversityHsinchuTaiwan, R.O.C.

Personalised recommendations