Skip to main content
SpringerLink
Log in

Oscillation analysis for salient object detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Salient object detection from an image is important for many multimedia applications. Existing methods provide good solutions to saliency detection; however, their results often emphasize the high-contrast edges, instead of regions/objects. In this paper, we present a method for salient object detection based on oscillation analysis. Our study shows that salient objects and their backgrounds have different amplitudes of oscillation between the local minima and maxima. Based on this observation, our method analyzes the oscillation in an image by estimating its local minima and maxima and computes the saliency map according to the oscillation magnitude contrast. Our method detects the local minima and maxima and performs extreme interpolation to smoothly propagate these information to the whole image. In this way, the oscillation information is smoothly assigned to regions, retaining well-defined salient boundaries as there are large variations near the salient boundaries (edges between objects and their backgrounds). As a result, our saliency map highlights salient regions/objects instead of high-contrast boundaries. We experiment with our method on two large public data set. Our results demonstrate the effectiveness of our method. We further apply our salient object detection method to automatic salient object segmentation, which again shows the success.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Achanta R, Estrada F, Wils P, Susstrunk S (2008) Salient region detection and segmentation. In: International conference on computer vision systems

  2. Achanta R, Hemamiz S, Estraday F, Susstrunk S (2009) Frequency-tuned salient region detection. In: IEEE international conference on computer vision and pattern recognition

  3. Achanta R, Susstrunk S (2010) Saliency detection using maximum symmetric surround. In: International conference on image processing

  4. Cheng M, Zhang G, Mitra NJ, Huang X, Hu S (2011) Global contrast based salient region detection. In: IEEE conference on computer vision and pattern recognition, pp 409–416

  5. Christopoulos C, Skodras A, Koike A, Ebrahimi T (2000) The jpeg2000 still image coding system: an overview. IEEE Trans Consum Electron 46(4):1103–1127

    Article  Google Scholar 

  6. Duan L, Wu C, Miao J, Qing L, Fu Y (2011) Visual saliency detection by spatially weighted dissimilarity. In: IEEE conference on computer vision and pattern recognition, pp 473–480

  7. Fu Y, Cheng J, Li Z, Lu H (2008) Saliency cuts: an automatic approach to object segmentation. In: Proc. ICPR2008, pp 1–4

  8. Gijsenij A, Gevers T (2011) Color constancy using natural image statistics and scene semantics. IEEE Trans Pattern Anal Mach Intell 33(4):687–698

    Article  Google Scholar 

  9. Goferman S, Zelnik-Manor L, Tal A (2010) Context-aware saliency detection. In: IEEE conference on computer vision and pattern recognition, pp 2376–2383

  10. Gopalakrishnan V, Hu Y, Rajan D (2009) Random walks on graphs to model saliency in images. In: IEEE conference on computer vision and pattern recognition

  11. Han J, Ngan K, Li M, Zhang H (2006) Unsupervised extraction of visual attention objects in color images. IEEE Trans Circuits Syst Video Technol 16(1):141–145

    Article  Google Scholar 

  12. Harel J, Koch C, Perona P (2007) Graph-based visual saliency. Adv Neural Inf Process Syst 19:545–552

    Google Scholar 

  13. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: IEEE conference on computer vision and pattern recognition

  14. Hu Y, Rajan D, Chia LT (2005) Robust subspace analysis for detecting visual attention regions in images. In: ACM international conference on multimedia

  15. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259

    Article  Google Scholar 

  16. Ko BC, Nam JY (2006) Object-of-interest image segmentation based on human attention and semantic region clustering. J Opt Soc Am A 23(10):2462–2470

    Article  Google Scholar 

  17. Levin A, Lischinski D, Weiss Y (2004) Colorization using optimization. ACM Trans Graph 23(3):689–694

    Article  Google Scholar 

  18. Liu H, Xie X, Ma WY, Zhang HJ (2003) Automatic browsing of large pictures on mobile devices. In: Proceedings of the ACM international conference on multimedia, pp 148–155

  19. Liu T, Sun J, Zheng NN, Tang X, Shum HY (2007) Learning to detect a salient object. In: IEEE conference on computer vision and pattern recognition

  20. Ma YF, Zhang HJ (2003) Contrast-based image attention analysis by using fuzzy growing. In: ACM international conference on multimedia

  21. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  22. Rother C, Kolmogorov V, Blake A (2004) “grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23:309–314

    Article  Google Scholar 

  23. Rubinstein M, Gutierrez D, Sorkine O, Shamir A (2010) A comparative study of image retargeting. ACM Trans Graph 29(5):160:1–160:10

    Google Scholar 

  24. Setlur V, Takagi S, Raskar R, Gleicher M, Gooch B (2005) Automatic image retargeting. In: Proceedings of the 4th international conference on mobile and ubiquitous multimedia (MUM 2005), pp 59–68

  25. Subr K, Soler C, Durand F (2009) Edge-preserving multiscale image decomposition based on local extrema. ACM Trans Graph 28(5):147:1–147:9

    Article  Google Scholar 

  26. Suh B, Ling H, Bederson BB, Jacobs DW (2003) Automatic thumbnail cropping and its effectiveness. In: Proceedings of ACM UIST, pp 95–104

  27. Wang Z, Li B (2008) A two-stage approach to saliency detection in images. In: International conference on acoustics, speech and signal processing

  28. Yanulevskaya V, Geusebroek JM (2009) Significance of the weibull distribution and its sub-models in natural image statistics. In: International conference on computer vision theory and applications, pp 355–362

  29. Zhai Y, Shah M (2006) Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th annual ACM international conference on Multimedia, pp 815–824

Download references

Acknowledgements

We would like to thank the reviewers for their insightful and constructive comments. This research is supported by Shandong Provincial Natural Science Foundation of China (Grant No. ZR2011FM037) and Innovation Fund for Distinguished Graduate Student of Shandong University (Grant No. yyx10043).

Author information

Authors and Affiliations

  1. Shandong University, Jinan, Shandong, China

    Yang Liu & Xueqing Li

  2. Weifang College, Weifang, Shandong, China

    Lei Wang

  3. Portland State University, Portland, OR, USA

    Yuzhen Niu & Feng Liu

Authors
  1. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xueqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuzhen Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, Y., Li, X., Wang, L. et al. Oscillation analysis for salient object detection. Multimed Tools Appl 68, 659–679 (2014). https://doi.org/10.1007/s11042-012-1072-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1072-6

Keywords

Search

Navigation