Skip to main content
SpringerLink
Log in

Multi-resolution dynamic mode decomposition-based salient region detection in noisy images

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Detection of salient region in an image is a crucial problem in many cognition and computer vision applications like object detection, adaptive image compression, automatic image cropping, video and image analysis. A part of an image is considered as salient, if the set of pixels under consideration protrudes from the rest, in terms of features such as color, contrast and local orientations. Generally, computational models for saliency assume that the image under observation is clean and fails to deal with visual disturbances. This paper presents a robust method for the detection of salient regions, using the multi-resolution dynamic mode decomposition (MRDMD approach). Effectiveness of the proffered method for the detection of salient region within clean and noisy images was examined and successfully verified for a wide range of noise strengths.

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

Similar content being viewed by others

References

  1. Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. In: ACM Transactions on Graphics (TOG), vol. 26. ACM (2007)

  2. Fang, Y., Chen, Z., Lin, W., Lin, C.W.: Saliency detection in the compressed domain for adaptive image retargeting. IEEE Trans. Image Process. 21(9), 3888 (2012)

    Article  MathSciNet  Google Scholar 

  3. Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: International Conference on Computer Vision Systems, pp. 66–75. Springer, Berlin (2008)

  4. Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. In: Matters of Intelligence, pp. 115–141. Springer, Berlin (1987)

  5. 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 (1998)

    Article  Google Scholar 

  6. Borji, A., Cheng, M.M., Hou, Q., Jiang, H., Li, J.: Salient object detection: a survey (2014). arXiv preprint arXiv:1411.5878

  7. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 1597–1604 (2009)

  8. Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915 (2012)

    Article  Google Scholar 

  9. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems, pp. 545–552 (2007)

  10. Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H.: Saliency detection via graph-based manifold ranking. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3166–3173 (2013)

  11. Jiang, B., Zhang, L., Lu, H., Yang, C., Yang, M.H.: Saliency detection via absorbing Markov chain. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1665–1672 (2013)

  12. Kim, J., Han, D., Tai, Y.W., Kim, J.: Salient region detection via high-dimensional color transform and local spatial support. IEEE Trans. Image Process. 25(1), 9 (2016)

    Article  MathSciNet  Google Scholar 

  13. Sikha, O., Kumar, S.S., Soman, K.: Salient region detection and object segmentation in color images using dynamic mode decomposition. J. Comput. Sci. 25, 351–366 (2017)

    Article  Google Scholar 

  14. Li, H., Wu, E., Wu, W.: Salient region detection via locally smoothed label propagation: with application to attention driven image abstraction. Neurocomputing 230, 359 (2017)

    Article  Google Scholar 

  15. Annum, R., Riaz, M.M., Ghafoor, A.: Saliency detection using contrast enhancement and texture smoothing operations. Signal Image Video Process. 12(3), 505 (2018)

    Article  Google Scholar 

  16. Li, W., Feng, S., Guan, H.P., Zhan, Z., Gong, C.: Video saliency detection based on low-level saliency fusion and saliency-aware geodesic. J. Electron. Imaging 28(1), 013009 (2019)

    Article  Google Scholar 

  17. Bruce, N.D., Tsotsos, J.K.: Saliency, attention, and visual search: an information theoretic approach. J. Vis. 9(3), 5 (2009)

    Article  Google Scholar 

  18. Mohan, N., Soman, K., Kumar, S.S.: A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model. Appl. Energy 232, 229 (2018)

    Article  Google Scholar 

  19. Grosek, J., Kutz, J.N.: Dynamic mode decomposition for real-time background/foreground separation in video (2014). arXiv preprint arXiv:1404.7592

  20. Kutz, J.N., Fu, X., Brunton, S.L., Erichson. N.B.: Multi-resolution dynamic mode decomposition for foreground/background separation and object tracking. In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 921–929 (2015)

  21. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679 (1986)

    Article  Google Scholar 

  22. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600 (2004)

    Article  Google Scholar 

  23. Zhang, J., Sclaroff, S., Lin, Z., Shen, X., Price, B., Mech, R.: Minimum barrier salient object detection at 80 fps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1404–1412 (2015)

  24. Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569 (2015)

    Article  Google Scholar 

  25. Wang, L., Xue, J., Zheng, N., Hua, G.: Automatic salient object extraction with contextual cue. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 105–112 (2011)

  26. Judd, T., Durand, F., Torralba, A.: A benchmark of computational models of saliency to predict human fixations. Computer Science and Artificial Intelligence Lab (CSAIL), CASIL Technical Reports. Massachusetts Institute of Technology, MA, USA (2012)

  27. Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: a Bayesian framework for saliency using natural statistics. J. Vis. 8(7), 32 (2008)

    Article  Google Scholar 

  28. Tavakoli, H.R., Rahtu, E., Heikkilä, J.: Fast and efficient saliency detection using sparse sampling and kernel density estimation. In: Scandinavian Conference on Image Analysis, pp. 666–675. Springer, Berlin (2011)

  29. Murray, N., Vanrell, M., Otazu, X., Parraga, C.A.: Saliency estimation using a non-parametric low-level vision model. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 433–440 (2011)

  30. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp. 1–8 (2007)

  31. Seo, H.J., Milanfar, P.: Static and space–time visual saliency detection by self-resemblance. J. Vis. 9(12), 15 (2009)

    Article  Google Scholar 

  32. Duan, L., Wu, C., Miao, J., Qing, L., Fu, Y.: Visual saliency detection by spatially weighted dissimilarity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 473–480 (2011)

  33. Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: International Conference on Computer Vision Systems, pp. 66–75. Springer, Berlin (2008)

  34. Cheng, M.M., Warrell, J., Lin, W.Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1529–1536 (2013)

  35. Kim, C., Milanfar, P.: Visual saliency in noisy images. J. Vis. 13(4), 5 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Computational Engineering and Networking (CEN), Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    O. K. Sikha

  2. Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    K. P. Soman

Authors
  1. O. K. Sikha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. P. Soman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to O. K. Sikha.

Ethics declarations

Conflict of interest

We declare that this work is not funded by any of the funding agencies, and we have no other potential conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sikha, O.K., Soman, K.P. Multi-resolution dynamic mode decomposition-based salient region detection in noisy images. SIViP 14, 167–175 (2020). https://doi.org/10.1007/s11760-019-01539-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01539-9

Keywords

Search

Navigation