Skip to main content

Advertisement

Springer Nature Link
Log in

Visual Saliency Using Binary Spectrum of Walsh–Hadamard Transform and Its Applications to Ship Detection in Multispectral Imagery

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Detection of visual saliency is valuable for applications like robot navigation, adaptive image compression, and object recognition. In this paper, we propose a fast frequency domain visual saliency method by use of the binary spectrum of Walsh–Hadamard transform (WHT). The method achieves saliency detection by simply exploiting the WHT components of the scene under view. Unlike space domain-based approaches, our method performs the cortical center-surround suppression in frequency domain and thus has implicit biological plausibility. By virtue of simplicity and speed of the WHT, the proposed method is very simple and fast in computation, and outperforms existing state-of-the-art saliency detection methods, when evaluated by using the capability of eye fixation prediction. In arduous tasks of ship detection in multispectral imagery, large amount of multispectral data require real-time processing and analyzing. As a very fast and effective technique for saliency detection, the proposed method is modified and applied to automatic ship detection in multispectral imagery. The robustness of the method against sea clutters is further proved.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Itti L, Koch C (2001) Computational modeling of visual attention. Nat Rev Neurosci 2(3):194–203

    Article  Google Scholar 

  2. Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12(1):97–136

    Article  Google Scholar 

  3. Koch C, Ullman S (1985) Shifts in selective visual attention: towards the underlying neural circuitry. Hum Neurobiol 4(4):219–227

    Google Scholar 

  4. 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 

  5. Bruce NDB, Tsotsos JK (2005) Saliency based on information maximization. In: Proceedings of NIPS, pp 155–162

  6. Bruce NDB, Tsotsos JK (2009) Saliency, attention, and visual search: an information theoretic approach. J Vis 9(3):1–24

    Article  Google Scholar 

  7. Harel J, Koch C, Perona P (2006) Graph-based visual saliency. In: Proceedings of NIPS, pp 545–552

  8. Gao D, Han S, Vasconcelos N (2009) Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition. IEEE Trans Pattern Anal Mach Intell 31(6):989–1005

    Article  Google Scholar 

  9. Gao D, Mahadevan V, Vasconcelos N (2008) On the plausibility of the discriminant center-surround hypothesis for visual saliency. J Vis 8(7):1–18

    Article  Google Scholar 

  10. Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. Lecture Notes in computer science, LNCS 5008, pp 66–75

  11. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. IEEE international conference on computer vision and pattern recognition (CVPR), pp 1597–1604

  12. Goferman S, Zelnik-Manor L, Tal A (2010) Context-aware saliency detection. In: Proceedings of CVPR, pp 2376–2383

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

  14. Cheng M-M, Mitra NJ, Huang X, Torr PHS, Hu S-M (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37(3):569–582

    Article  Google Scholar 

  15. Wang W, Wang Y, Huang Q, Gao W (2010) Measuring visual saliency by site entropy rate. IEEE conference on computer vision and pattern recognition (CVPR), pp 2368–2375

  16. Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum H-Y (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33(2):353–367

    Article  Google Scholar 

  17. Tian H, Fang Y, Zhao Y, Lin W, Ni R, Zhu Z (2014) Salient region detection by fusing bottom-up and top-down features extracted from a single image. IEEE Trans Image Process 23(10):4389–4398

    Article  MathSciNet  Google Scholar 

  18. Tao D, Cheng J, Song M, Lin X (2015) Manifold ranking-based matrix factorization for saliency detection. IEEE Trans Neural Netw Learn Syst, to be published

  19. Shi J, Yan Q, Xu L, Jia J (2015) Hierarchical saliency detection on extended CSSD. IEEE Trans Pattern Anal Mach Intell, to be published

  20. Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. IEEE conference on computer vision and pattern recognition (CVPR), pp 1155–1162

  21. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: Proceedings of CVPR, pp 1–8

  22. Guo C, Ma Q, Zhang L (2008) Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. IEEE conference on computer vision and pattern recognition (CVPR), pp 1–8

  23. Guo C, Zhang L (2010) A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185–198

    Article  MathSciNet  Google Scholar 

  24. Yu Y, Wang B, Zhang L (2009) Hebbian-based neural networks for bottom-up visual attention systems. In: 16th International conference on neural information processing, (ICONIP), LNCS 5863, (Part I), pp 1–9

  25. Yu Y, Wang B, Zhang L (2011) Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images. Neurocomputing 74(11):2008–2017

    Article  Google Scholar 

  26. Ahmed N, Rao KR, Ahmed N, Rao KR (1975) Walsh–Hadamard transform. In: Ahmed N, Rao KR (eds) Orthogonal transforms for digital signal processing. Springer, New York, pp 99–152

    Chapter  Google Scholar 

  27. Fino BJ, Algazi VR (1976) Unified matrix treatment of the fast Walsh–Hadamard transform. IEEE Trans Comput C–25(11):1142–1146

    Article  MathSciNet  MATH  Google Scholar 

  28. Kunz HO (1979) On the equivalence between one-dimensional discrete Walsh–Hadamard and multidimensional discrete Fourier transforms. IEEE Trans Comput C–28(3):267–268

    Article  MathSciNet  MATH  Google Scholar 

  29. Yu X, Hoff LE, Reed IS, Chen AM, Stotts LB (1997) Automatic target detection and recognition in multiband imagery: a unified ML detection and estimation approach. IEEE Trans Image Process 6(1):143–156

    Article  Google Scholar 

  30. Reed IS, Yu X (1990) Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans Acoust Speech Signal Process 38(10):1760–1770

    Article  Google Scholar 

  31. Karacali B, Snyder W (2002) Automatic target detection using multispectral imaging. In: Proceedings of the 31st applied image pattern recognition workshop (AIPR), pp 55–62

  32. Chen JY, Reed IS (1987) A detection algorithm for optical targets in clutter. IEEE Trans Aerosp Electron Syst 23(1):46–59

    Article  Google Scholar 

  33. Yu X, Reed IS (1993) Comparative performance analysis of adaptive multispectral detectors. IEEE Trans Signal Process 41(8):2639–2656

    Article  MATH  Google Scholar 

  34. Li Z (2002) A saliency map in primary visual cortex. Trends Cogn Sci 6(1):9–16

    Article  Google Scholar 

  35. Li Z (2006) Theoretical understanding of the early visual processes by data compression and data selection. Netw Comput Neural Syst 17(4):301–334

    Article  Google Scholar 

  36. Li Z, Dayan P (2006) Pre-attentive visual selection. Neural Netw 19(9):1437–1439

    Article  Google Scholar 

  37. Itti L, Baldi PF (2005) Bayesian surprise attracts human attention. In: Proceedings of NIPS, pp 547–554

  38. Torralba A, Oliva A, Castelhano MS, Henderson JM (2006) Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychol Rev 113(4):766–786

    Article  Google Scholar 

  39. Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW (2008) SUN: a Bayesian framework for saliency using natural statistics. J Vis 8(7):1–20

    Article  Google Scholar 

  40. Barlow HB (1961) Possible principles underlying the transformation of sensory messages. In: Rosenblith WA (ed) Sensory communication. M.I.T. Press, Cambridge, pp 217–234

    Google Scholar 

  41. Petrov Y, Li Z (2003) Local correlations, information redundancy, and sufficient pixel depth in natural images. J Opt Soc Am A 20(1):56–66

    Article  Google Scholar 

  42. Li Z, Atick JJ (1994) Toward a theory of the striate cortex. Neural Comput 6(1):127–146

    Article  Google Scholar 

  43. Tatler BW, Baddeley RJ, Gilchrist ID (2005) Visual correlates of fixation selection: effects of scale and time. Vis Res 45(5):643–659

    Article  Google Scholar 

  44. Scholl BJ (2001) Objects and attention: the state of the art. Cognition 80(1–2):1–46

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 61263048), by the Scientific Research Foundation of Yunnan Provincial Department of Education (2012Y277), by the Scientific Research Project of Yunnan University (2011YB21), and by the Young and Middle-Aged Backbone Teachers’ Cultivation Plan of Yunnan University (XT412003).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Ying Yu & Jian Yang

Authors
  1. Ying Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jian Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ying Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Yang, J. Visual Saliency Using Binary Spectrum of Walsh–Hadamard Transform and Its Applications to Ship Detection in Multispectral Imagery. Neural Process Lett 45, 759–776 (2017). https://doi.org/10.1007/s11063-016-9507-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-016-9507-0

Keywords