Advertisement

Bio-inspired Visual Saliency Detection and Its Application on Image Retargeting

  • Lijuan Duan
  • Chunpeng Wu
  • Haitao Qiao
  • Jili Gu
  • Jun Miao
  • Laiyun Qing
  • Zhen Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7062)

Abstract

In this paper, we present a saliency guided image retargeting method. Our bio-inspired saliency measure integrates three factors: dissimilarity, spatial distance and central bias, and these three factors are supported by research on human vision system (HVS). To produce perceptual satisfactory retargeting images, we use the saliency map as the importance map in the retargeting method. We suppose that saliency maps can indicate informative regions, and filter out background in images. Experimental results demonstrate that our method outperforms previous retargeting method guided by the gray image on distorting dominant objects less. And further comparison between various saliency detection methods show that retargeting method using our saliency measure maintains more parts of foreground.

Keywords

visual saliency dissimilarity spatial distance central bias image retargeting 

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 TPAMI 20, 1254–1259 (1998)CrossRefGoogle Scholar
  2. 2.
    Gao, D., Vasconcelos, N.: Bottom-Up Saliency is a Discriminant Process. IEEE ICCV, 1–6 (2007)Google Scholar
  3. 3.
    Murray, N., Vanrell, M., Otazu, X., Parraga, C.A.: Saliency Estimation Using A Non- Parametric Low-Level Vision Model. IEEE CVPR, 433–440 (2011)Google Scholar
  4. 4.
    Kanan, C., Cottrell, G.: Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. IEEE CVPR, 2472–2479 (2010)Google Scholar
  5. 5.
    Yu, H., Li, J., Tian, Y., Huang, H.: Automatic Interesting Object Extraction from Images Using Complementary Saliency Maps. ACM Multimedia, 891–894 (2010)Google Scholar
  6. 6.
    Navalpakkam, V., Itti, L.: An Intergrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed. IEEE CVPR, 2049–2056 (2006)Google Scholar
  7. 7.
    Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual Saliency Detection by Spatially Weighted Dissimilarity. IEEE CVPR, 473–480 (2011)Google Scholar
  8. 8.
    Levelthal, A.G.: The Neural Basis of Visual Function: Vision and Visual Dysfunction. CRC Press, Fla (1991)Google Scholar
  9. 9.
    Rajashekar, U., van der Linde, I., Bovik, A.C., Cormack, L.K.: Foveated Analysis of Image Features at Fixations. Vision Research 47, 3160–3172 (2007)Google Scholar
  10. 10.
    Wandell, B.A.: Foundations of vision. Sinauer Associates (1995)Google Scholar
  11. 11.
    Tatler, B.W.: The Central Fixation Bias in Scene Viewing: Selecting an Optimal Viewing Position Independently of Motor Biased and Image Feature Distributions. J. Vision 7(4), 1–17 (2007)CrossRefGoogle Scholar
  12. 12.
    Zhao, Q., Koch, C.: Learning A Saliency Map Using Fixated Locations in Natural Scenes. J. Vision 11(9), 1–15 (2011)CrossRefGoogle Scholar
  13. 13.
    Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: A Comparative Study of Image Retargeting. ACM Trans. Graphics 29(160), 1–10 (2010)CrossRefGoogle Scholar
  14. 14.
    Shamir, A., Sorkine, O.: Visual Media Retargeting. ACM SIGGRAPH Asia Courses (11), 1–11 (2009)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Tai, C., Sorkine, O., Lee, T.: Optimized Scale-and-Stretch for Image Resizing. ACM Trans. Graphics 27(118), 1–8 (2008)Google Scholar
  16. 16.
    Rubinstein, M., Shamir, A., Avidan, S.: Improved Seam Carving for Video Retargeting. ACM Trans. Graphics 3(16), 1–9 (2008)CrossRefGoogle Scholar
  17. 17.
    Viola, P., Jones, M.: Robust Real-Time Object Detection. IJCV 57, 137–154 (2001)CrossRefGoogle Scholar
  18. 18.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. IEEE CVPR, 886–893 (2005)Google Scholar
  19. 19.
    Felzenszwaklb, P., Girshick, R., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part Based Models. IEEE TPAMI 32, 1627–1645 (2010)CrossRefGoogle Scholar
  20. 20.
    Bruce, N.D.B., Tsotsos, J.K.: Saliency Based on Information Maximization. NIPS, 155–162 (2005)Google Scholar
  21. 21.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to Predict Where Humans Look. IEEE ICCV, 2106–2113 (2009)Google Scholar
  22. 22.
    Hou, X., Zhang, L.: Dynamic Visual Attention: Searching for Coding Length Increments. In: NIPS, pp. 681–688 (2008)Google Scholar
  23. 23.
    Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. In: NIPS, pp. 545–552 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lijuan Duan
    • 1
  • Chunpeng Wu
    • 1
  • Haitao Qiao
    • 1
  • Jili Gu
    • 1
  • Jun Miao
    • 2
  • Laiyun Qing
    • 3
  • Zhen Yang
    • 1
  1. 1.College of Computer Science and TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  3. 3.School of Information Science and EngineeringGraudate University of the Chinese Academy of SciencesBeijingChina

Personalised recommendations