Skip to main content

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

  • Conference paper
Book cover Neural Information Processing (ICONIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7062))

Included in the following conference series:

  • 3801 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE TPAMI 20, 1254–1259 (1998)

    Article  Google Scholar 

  2. Gao, D., Vasconcelos, N.: Bottom-Up Saliency is a Discriminant Process. IEEE ICCV, 1–6 (2007)

    Google Scholar 

  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. Kanan, C., Cottrell, G.: Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. IEEE CVPR, 2472–2479 (2010)

    Google Scholar 

  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. 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. 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. Levelthal, A.G.: The Neural Basis of Visual Function: Vision and Visual Dysfunction. CRC Press, Fla (1991)

    Google Scholar 

  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. Wandell, B.A.: Foundations of vision. Sinauer Associates (1995)

    Google Scholar 

  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)

    Article  Google Scholar 

  12. Zhao, Q., Koch, C.: Learning A Saliency Map Using Fixated Locations in Natural Scenes. J. Vision 11(9), 1–15 (2011)

    Article  Google Scholar 

  13. Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: A Comparative Study of Image Retargeting. ACM Trans. Graphics 29(160), 1–10 (2010)

    Article  Google Scholar 

  14. Shamir, A., Sorkine, O.: Visual Media Retargeting. ACM SIGGRAPH Asia Courses (11), 1–11 (2009)

    Article  Google Scholar 

  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. Rubinstein, M., Shamir, A., Avidan, S.: Improved Seam Carving for Video Retargeting. ACM Trans. Graphics 3(16), 1–9 (2008)

    Article  Google Scholar 

  17. Viola, P., Jones, M.: Robust Real-Time Object Detection. IJCV 57, 137–154 (2001)

    Article  Google Scholar 

  18. Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. IEEE CVPR, 886–893 (2005)

    Google Scholar 

  19. Felzenszwaklb, P., Girshick, R., McAllester, D., Ramanan, D.: Object Detection with Discriminatively Trained Part Based Models. IEEE TPAMI 32, 1627–1645 (2010)

    Article  Google Scholar 

  20. Bruce, N.D.B., Tsotsos, J.K.: Saliency Based on Information Maximization. NIPS, 155–162 (2005)

    Google Scholar 

  21. Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to Predict Where Humans Look. IEEE ICCV, 2106–2113 (2009)

    Google Scholar 

  22. Hou, X., Zhang, L.: Dynamic Visual Attention: Searching for Coding Length Increments. In: NIPS, pp. 681–688 (2008)

    Google Scholar 

  23. Harel, J., Koch, C., Perona, P.: Graph-Based Visual Saliency. In: NIPS, pp. 545–552 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Duan, L. et al. (2011). Bio-inspired Visual Saliency Detection and Its Application on Image Retargeting. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24955-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24954-9

  • Online ISBN: 978-3-642-24955-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics