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A Parametric Spectral Model for Texture-Based Salience

  • Kasim Terzić
  • Sai Krishna
  • J. M. H. du Buf
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

We present a novel saliency mechanism based on texture. Local texture at each pixel is characterised by the 2D spectrum obtained from oriented Gabor filters. We then apply a parametric model and describe the texture at each pixel by a combination of two 1D Gaussian approximations. This results in a simple model which consists of only four parameters. These four parameters are then used as feature channels and standard Difference-of-Gaussian blob detection is applied in order to detect salient areas in the image, similar to the Itti and Koch model. Finally, a diffusion process is used to sharpen the resulting regions. Evaluation on a large saliency dataset shows a significant improvement of our method over the baseline Itti and Koch model.

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Notes

Acknowledgements

This work was supported by the EU under the FP-7 grant ICT-2009.2.1-270247 NeuralDynamics and by the FCT under the grants LarSYS UID/EEA/50009/2013 and SparseCoding EXPL/EEI-SII/1982/2013.

References

  1. 1.
    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
  2. 2.
    Achanta, R., Hemami, S.S., Estrada, F.J., Süsstrunk, S.: Frequency-tuned salient region detection. In: CVPR, pp. 1597–1604 (2009)Google Scholar
  3. 3.
    du Buf, J.: Abstract processes in texture discrimination. Spat. Vis. 6, 221–242 (1992)CrossRefGoogle Scholar
  4. 4.
    du Buf, J.: Improved grating and bar cell models in cortical area V1 and texture coding. Image Vis. Comput. 25, 873–882 (2007)CrossRefGoogle Scholar
  5. 5.
    Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H.S., Hu, S.M.: Global contrast based salient region detection. IEEE T-PAMI 37(3), 569–582 (2015)CrossRefGoogle Scholar
  6. 6.
    Cheng, M., Zhang, G., Mitra, N.J., Huang, X., Hu, S.: Global contrast based salient region detection. In: CVPR, pp. 409–416 (2011)Google Scholar
  7. 7.
    Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: CVPR, pp. 473–480 (2011)Google Scholar
  8. 8.
    Frintrop, S., Werner, T., Martin-Garcia, G.: Traditional saliency reloaded: a good old model in new shape. In: CVPR (2015)Google Scholar
  9. 9.
    Gao, D., Vasconcelos, N.: Bottom-up saliency is a discriminant process. In: ICCV, pp. 1–6 (2007)Google Scholar
  10. 10.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: CVPR, pp. 2376–2383 (2010)Google Scholar
  11. 11.
    Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: CVPR (2008)Google Scholar
  12. 12.
    Han, J., Ngan, K.N., Li, M., Zhang, H.: Unsupervised extraction of visual attention objects in color images. IEEE Trans. Circuits Syst. Video Techn. 16(1), 141–145 (2006)CrossRefGoogle Scholar
  13. 13.
    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: NIPS, pp. 545–552 (2006)Google Scholar
  14. 14.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR (2007)Google Scholar
  15. 15.
    Hu, Y., Xie, X., Ma, W.-Y., Chia, L.-T., Rajan, D.: Salient region detection using weighted feature maps based on the human visual attention model. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3332, pp. 993–1000. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  16. 16.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res. 40(10–12), 1489–1506 (2000)CrossRefGoogle Scholar
  17. 17.
    Itti, L., Baldi, P.: Bayesian surprise attracts human attention. In: NIPS, pp. 547–554 (2005)Google Scholar
  18. 18.
    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
  19. 19.
    Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N.: Salient object detection: a discriminative regional feature integration approach. In: CVPR (2013)Google Scholar
  20. 20.
    Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290(5802), 91–97 (1981)CrossRefGoogle Scholar
  21. 21.
    Lee, T.S., Mumford, D., Romero, R., Lamme, V.F.: The role of the primary visual cortex in higher level vision. Vision Res. 38, 2429–2454 (1998)CrossRefGoogle Scholar
  22. 22.
    Li, Y., Hou, X., Koch, C., Rehg, J.M., Yuille, A.L.: The secrets of salient object segmentation. In: CVPR, pp. 280–287 (2014)Google Scholar
  23. 23.
    Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. In: CVPR (2007)Google Scholar
  24. 24.
    Neumann, B., Terzić, K.: Context-based probabilistic scene interpretation. In: IFIP AI, pp. 155–164, September 2010Google Scholar
  25. 25.
    Parkhurst, D., Law, K., Niebur, E.: Modeling the role of salience in the allocation of overt visual attention. Vision Res. 42(1), 107–123 (2002)CrossRefGoogle Scholar
  26. 26.
    Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: Contrast based filtering for salient region detection. In: IEEE CVPR, pp. 733–740 (2012)Google Scholar
  27. 27.
    Self, M.W., van Kerkoerle, T., Super, H., Roelfsema, P.R.: Distinct roles of the cortical layers of area V1 in figure-ground segregation. Curr. Biol. 23, 2121–2129 (2013)CrossRefGoogle Scholar
  28. 28.
    Terzić, K., Hotz, L., Šochman, J.: Interpreting structures in man-made scenes: combining low-level and high-level structure sources. In: International Conference on Agents and Artificial Intelligence. Valencia, Spain, January 2010Google Scholar
  29. 29.
    Terzić, K., Lobato, D., Saleiro, M., Martins, J., Farrajota, M., Rodrigues, J.M.F., du Buf, J.M.H.: Biological models for active vision: towards a unified architecture. In: Chen, M., Leibe, B., Neumann, B. (eds.) ICVS 2013. LNCS, vol. 7963, pp. 113–122. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  30. 30.
    Terzić, K., Rodrigues, J., du Buf, J.: Fast cortical keypoints for real-time object recognition. In: ICIP, pp. 3372–3376. Melbourne, September 2013Google Scholar
  31. 31.
    Terzić, K., Rodrigues, J., du Buf, J.: BIMP: a real-time biological model of multi-scale keypoint detection in V1. Neurocomputing 150, 227–237 (2015)CrossRefGoogle Scholar
  32. 32.
    Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurosci. 5, 495–501 (2004)CrossRefGoogle Scholar
  33. 33.
    Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Kasim Terzić
    • 1
  • Sai Krishna
    • 1
  • J. M. H. du Buf
    • 1
  1. 1.Vision Laboratory/LARSysUniversity of the AlgarveFaroPortugal

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