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Multimedia Tools and Applications

, Volume 76, Issue 13, pp 14781–14798 | Cite as

Small target detection combining regional stability and saliency in a color image

  • Jing Lou
  • Wei Zhu
  • Huan Wang
  • Mingwu Ren
Article
  • 524 Downloads

Abstract

In this paper, we will address the issue of detecting small target in a color image from the perspectives of both stability and saliency. First, we consider small target detection as a stable region extraction problem. Several stability criteria are applied to generate a stability map, which involves a set of locally stable regions derived from sequential boolean maps. Second, considering the local contrast of a small target and its surroundings, we obtain a saliency map by comparing the color vector of each pixel with its Gaussian blurred version. Finally, both the stability and saliency maps are integrated in a pixel-wise multiplication manner for removing false alarms. In addition, we introduce a set of integration models by combining several existing stability and saliency methods, and use them to indicate the validity of the proposed framework. Experimental results show that our model adapts to target size variations and performs favorably in terms of precision, recall and F-measure on three challenging datasets.

Keywords

Small target detection Stable region Visual saliency Color image 

Notes

Acknowledgments

The authors thank all of the anonymous reviewers for their insights and suggestions, which were very helpful in improving this manuscript. They thank Haiyang Zhang for useful discussions. They also thank Mei Zhang and Huaiping Zhang for their kind proofreading of the manuscript. This work is supported by the National Natural Science Foundation of China under Grant 61231014.

References

  1. 1.
    Achanta R, Süsstrunk S (2010) Saliency detection using maximum symmetric surround. In: Proc. IEEE Int. Conf. Image Process., 2653–2656Google Scholar
  2. 2.
    Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation. In: Proc. Int. Conf. Comput. Vis. Syst., 66–75Google Scholar
  3. 3.
    Achanta R, Hemami S, Estrada F, Süsstrunk S (2009) Frequency-tuned salient region detection. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 1597–1604Google Scholar
  4. 4.
    Bae T-W, Zhang F, Kweon I-S (2012) Edge directional 2D LMS filter for infrared small target detection. Infrared Phys Technol 55(1):137–145CrossRefGoogle Scholar
  5. 5.
    Borji A, Cheng M-M, Jiang H, Li J (2015) Salient object detection: a benchmark. IEEE Trans Image Process 24(12):5706–5722MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen H-Y, Leou J-J (2012) Multispectral and multiresolution image fusion using particle swarm optimization. Multimed Tools Appl 60(3):495–518CrossRefGoogle Scholar
  7. 7.
    Chen CLP, Li H, Wei Y, Xia T, Tang YY (2014) A local contrast method for small infrared target detection. IEEE Trans Geosci Remote Sens 52(1):574–581CrossRefGoogle Scholar
  8. 8.
    Dragon R, Ostermann J, Van Gool L (2013) Robust realtime motion-split-and-merge for motion segmentation. In Proc. Ger. Conf. Pattern Recognit., 425–434Google Scholar
  9. 9.
    Erdem E, Erdem A (2013) Visual saliency estimation by nonlinearly integrating features using region covariances. J Vis 13(4):1–20, 11MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gao C, Meng D, Yang Y, Wang Y, Zhou X, Hauptmann AG (2013) Infrared patch-image model for small target detection in a single image. IEEE Trans Image Process 22(12):4996–5009MathSciNetCrossRefGoogle Scholar
  11. 11.
    Gonzalez RC, Woods RE (2002) Introduction. In: Digit. Image Process., 2nd ed. Prentice Hall. ch. 1: 12–13Google Scholar
  12. 12.
    Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 1–8Google Scholar
  13. 13.
    Kim S, Lee J (2012) Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track. Pattern Recogn 45(1):393–406CrossRefGoogle Scholar
  14. 14.
    Lee E, Gu E, Park K (2015) Effective small target enhancement and detection in infrared images using saliency map and image intensity. Opt Rev 22(4):659–668CrossRefGoogle Scholar
  15. 15.
    Li W, Pan C, Liu L-X (2009) Saliency-based automatic target detection in forward looking infrared images. In: Proc. IEEE Int. Conf. Image Process., 957–960Google Scholar
  16. 16.
    Li J, Levine MD, An X, Xu X, He H (2013) Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans Pattern Anal Mach Intell 35(4):996–1010CrossRefGoogle Scholar
  17. 17.
    Li Y, Liang S, Bai B, Feng D (2014) Detecting and tracking dim small targets in infrared image sequences under complex backgrounds. Multimed Tools Appl 71(3):1179–1199CrossRefGoogle Scholar
  18. 18.
    Matas J, Chum O, Urban M, Pajdla T (2004) Robust wide-baseline stereo from maximally stable extremal regions. Image Vis Comput 22(10):761–767CrossRefGoogle Scholar
  19. 19.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66CrossRefGoogle Scholar
  20. 20.
    Qi S, Ma J, Tao C, Yang C, Tian J (2013) A robust directional saliency-based method for infrared small-target detection under various complex backgrounds. IEEE Geosci Remote Sens Lett 10(3):495–499CrossRefGoogle Scholar
  21. 21.
    Vedaldi A, Fulkerson B (2008) VLFeat: An open and portable library of computer vision algorithms, version 0.9.19. http://www.vlfeat.org
  22. 22.
    Yang C, Zhang L, Lu H (2013) Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Process Lett 20(7):637–640CrossRefGoogle Scholar
  23. 23.
    Zhang W, Cong M, Wang L (2003) Algorithms for optical weak small targets detection and tracking: Review. In: Proc. IEEE Int. Conf. Neural Networks Signal Process., 643–647Google Scholar
  24. 24.
    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, 32CrossRefGoogle Scholar
  25. 25.
    Zhou C, Liu C (2015) An efficient segmentation method using saliency object detection. Multimed Tools Appl 74(15):5623–5634CrossRefGoogle Scholar
  26. 26.
    Zhu B, Xin Y (2015) Effective and robust infrared small target detection with the fusion of polydirectional first order derivative images under facet model. Infrared Phys Technol 69:136–144CrossRefGoogle Scholar
  27. 27.
    Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2814–2821Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingPeople’s Republic of China

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