Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation

  • Hamed Rezazadegan Tavakoli
  • Esa Rahtu
  • Janne Heikkilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

Salient region detection has gained a great deal of attention in computer vision. It is useful for applications such as adaptive video/image compression, image segmentation, anomaly detection, image retrieval, etc. In this paper, we study saliency detection using a center-surround approach. The proposed method is based on estimating saliency of local feature contrast in a Bayesian framework. The distributions needed are estimated particularly using sparse sampling and kernel density estimation. Furthermore, the nature of method implicitly considers what refereed to as center bias in literature. Proposed method was evaluated on a publicly available data set which contains human eye fixation as ground-truth. The results indicate more than 5% improvement over state-of-the-art methods. Moreover, the method is fast enough to run in real-time.

Keywords

Saliency detection discriminant center-surround eye-fixation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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, 1254–1259 (1998)CrossRefGoogle Scholar
  2. 2.
    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), http://icvs2008.info/index.htm CrossRefGoogle Scholar
  3. 3.
    Seo, H.J., Milanfar, P.: Training-free, generic object detection using locally adaptive regression kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1688–1704 (2010)CrossRefGoogle Scholar
  4. 4.
    Rahtu, E., Heikkilä, J.: A simple and efficient saliency detector for background subtraction. In: Proc. the 9th IEEE International Workshop on Visual Surveillance (VS 2009), Kyoto, Japan, pp. 1137–1144 (2009), http://www.ee.oulu.fi/mvg/page/saliency
  5. 5.
    Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008), doi:10.1109/CVPR.2008.4587715Google Scholar
  6. 6.
    Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8 (2007), doi:10.1109/CVPR.2007.383267Google Scholar
  7. 7.
    Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned Salient Region Detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Miami Beach, Florida (2009), http://www.cvpr2009.org/
  8. 8.
    Tsotsos, J.K., Bruce, N.D.B.: Saliency based on information maximization. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems 18, pp. 155–162. MIT Press, Cambridge (2006)Google Scholar
  9. 9.
    Lin, Y., Fang, B., Tang, Y.: A computational model for saliency maps by using local entropy. In: AAAI Conference on Artificial Intelligence (2010)Google Scholar
  10. 10.
    Mahadevan, V., Vasconcelos, N.: Spatiotemporal saliency in dynamic scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 171–177 (2010)CrossRefGoogle Scholar
  11. 11.
    Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010), http://www.ee.oulu.fi/mvg/page/saliency CrossRefGoogle Scholar
  12. 12.
    Gao, D., Mahadevan, V., Vasconcelos, N.: On the plausibility of the discriminant center-surround hypothesis for visual saliency. Journal of Vision 8(7) (2008), http://www.journalofvision.org/content/8/7/13.abstract, doi:10.1167/8.7.13
  13. 13.
    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) (2008), http://www.journalofvision.org/content/8/7/32.abstract, doi:10.1167/8.7.32
  14. 14.
    Yang, Y., Song, M., Li, N., Bu, J., Chen, C.: What is the chance of happening: A new way to predict where people look. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 631–643. Springer, Heidelberg (2010), http://dx.doi.org/10.1007/978-3-642-15555-0_46 CrossRefGoogle Scholar
  15. 15.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: IEEE International Conference on Computer Vision, ICCV (2009)Google Scholar
  16. 16.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2376–2383 (2010), doi:10.1109/CVPR.2010.5539929Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hamed Rezazadegan Tavakoli
    • 1
  • Esa Rahtu
    • 1
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision Group, Department of Electrical and Information EngineeringUniversity of OuluFinland

Personalised recommendations