Skip to main content
SpringerLink
Log in

Irregularity-based image regions saliency identification and evaluation

Multimedia Tools and Applications volume 75, pages 25–48 (2016)Cite this article

Abstract

Saliency or salient region extraction from images is still a challenging field as it needs some understanding of the image and its nature. A technique that is suitable for some applications is not necessarily useful in other applications, thus, saliency identification is dependent upon the application. Based on a survey of existing methods of saliency detection, a new technique to extract the salient regions from an image is proposed that utilizes local features of the region surrounding each pixel. The level of saliency is decided based on the irregularity of the region with compared to other regions. To make the process fully automatic, a new Fuzzy-based thresholding technique has also been developed. In addition to the above, a survey of existing saliency evaluation techniques has been carried out and we have proposed new evaluation methods. The proposed saliency extraction technique has been compared with other algorithms reported in the literature, and the results are discussed in detail.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

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
Fig. 17

References

  1. Achanta, R., Estrada, F., Wils, P. & SÄusstrunk, S., 2008. Salient Region Detection and Segmentation. s.l., s.n., pp. 66–75.

  2. Achanta, R., Hemami, S., Estrada, F. & Susstrunk, S., 2009. Frequency-tuned Salient Region Detection. s.l., IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009.

  3. Achanta, R. & Susstrunk, S., 2010 . Saliency detection using maximum symmetric surround. Hong Kong, s.n.

  4. Al-Azawi M, Yang Y, Istance H (2014) A new gaze points agglomerative clustering algorithm and its application in regions of interest extraction. IEEE International Advance Computing Conference (IACC), Gurgaon

    Book  Google Scholar 

  5. Anon., 2013. Precision and recall. [Online] Available at: https://en.wikipedia.org/wiki/Precision_and_recall [Accessed 15 8 2013].

  6. Arivazhagan S, Shebiah RN (2009) Object recognition using wavelet based salient points. Open Signal Process J 2:14–20

    Article  Google Scholar 

  7. Banerjee, M. & Kundu, M. K., 2003. Edge based features for content based image retrieval. Pattern Recognition, Issue 36, pp. 2649–2661

  8. Borji, A., Sihite, D. N. & Itti, L., 2012. Salient Object Detection: A Benchmark. Florence, Italy, 12th European Conference on Computer Vision Proceedings, Part II, pp. 414–429.

  9. Bruce, N., Loach, D. & Tsotsos, J., 2007. Visual Correlates of Fixation Selection: A Look at the Spatial Frequency Domain. San Antonio, TX, s.n.

  10. Burns, R. B. & Burns, R. A., 2008. Chapter 23: Cluster Analysis. In: Business Research Methods and Statistics Using SPSS. s.l.: Sage Publications Ltd., pp. 552–567.

  11. Choi YS, Mosley AD, Stark LW (1995) String editing analysis of human visual search. Optom Vis Sci 72:439–445

    Article  Google Scholar 

  12. Cook, D. & Swayne, D. F., 2007. chapter 5: Cluster Analysis. In: Interactive and Dynamic Graphics for Data Analysis. s.l.:Springer, pp. 103–128.

  13. Davis, J. & Sharma, V., 2004. Robust Background Subtraction for Person Detection in Thermal Imagery. s.l., Proc. of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’04), pp. 128–132.

  14. Einhauser W, Spain M, Perona P (2008) Objects predict fixations better than early saliency. J Vis 8(14–18):1–26

    Google Scholar 

  15. Erdem E, Erdem A (2013) Visual saliency estimation by nonlinearly integrating features using region covariances. J Vis 13(4–11):1–20

    Google Scholar 

  16. Estivill-Castro V (2002) Why so many clustering algorithms: a position paper. ACM SIGKDD Explor Newsl 4(1):65–75

    Article  MathSciNet  Google Scholar 

  17. Fang, Y., Chen, Z., Lin, W. & Lin, C.W., 2011. Saliency-based Image Retargeting in the Compressed Domain. s.l., ACM International Conference on Multimedia 2011 (ACM MM11).

  18. Fang Y, Chen Z, Lin W, Lin CW (2012) Saliency detection in the compressed domain for adaptive image retargeting. Trans Image Process 9(21):3888–3901

    Article  MathSciNet  Google Scholar 

  19. Fang Y et al (2012) Bottom–Up saliency detection model based on human visual sensitivity and amplitude spectrum. IEEE Trans Multimedia 14(1):187–198

    Article  Google Scholar 

  20. Gide, M. & Karam, L., 2012. Improved foveation- and saliency-based visual attention prediction under a quality assessment task. s.l., s.n., pp. 200–205 .

  21. Gopalakrishnan, V., Hu, Y. & Rajan, D., 2009. Random walks on graphs to model saliency in images. Miami, FL, s.n.

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

    Google Scholar 

  23. Hou, X. & Zhang, L., 2007. Saliency detection: A spectral residua approach.. s.l., s.n.

  24. Hou, X. & Zhang, . L., 2007. Saliency Detection: A Spectral Residual Approach. Minneapolis, MN, s.n.

  25. Hu, Y. et al., 2005. Salient Region Detection Using Weighted Feature Maps Based on the Human Visual Attention Model. s.l., Proc. Fifth Pacific Rim Conf. Multimedia, pp. 993–1,000.

  26. 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 

  27. Ivanoff RM, Klein J (2008) Inhibition of return. Scholarpedia 3(10):3650

    Article  Google Scholar 

  28. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) Surv 31(3):264–323

    Article  Google Scholar 

  29. Judd, T., Durand, . F. & Torralba, A., 2012. A Benchmark of Computational Models of Saliency to Predict Human Fixations, s.l.: CSAIL Technical Reports.

  30. Kadir T, Brady M (2001) Scale, saliency and image description. Int J Comput Vis 45(2):83–105

    Article  MATH  Google Scholar 

  31. Kapsalas, P., Rapantzikos, K., Sofou, A. & Avrithis, Y., 2008. Regions of interest for accurate object detection. London, s.n., pp. 147–154 .

  32. Kim C, Milanfar P (2013) Visual saliency in noisy images. J Vis 13(4–5):1–14

    Google Scholar 

  33. Kim, S., Park, S. & Kim, M., 2003. Central object extraction for object-based image retrieval, CIVR’03. Heidelberg, s.n., pp. 39–49.

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

    Google Scholar 

  35. Le Meur O, Callet PL, Barba D, Thoreau D (2006) A coherent computational approach to model bottom–Up visual attention. IEEE Trans Pattern Anal Mach Intell 28(5):802–817

    Article  Google Scholar 

  36. Li J et al (2007) Visual saliency based on scale-space analysis in the frequency domain. J Latex Class Files 6(1):1–16

    Google Scholar 

  37. Lin, Y., Fang, B. & Tang, Y., 2010. A Computational Model for Saliency Maps by Using Local Entropy. tlanta, Georgia, USA, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10).

  38. Lin DW, Yang SH (2007) Wavelet-based salient region extraction. In: advances in multimedia information processing—PCM 2007, vol 4810. Springer, Honk Kong, pp 389–392

    Book  Google Scholar 

  39. Lin Y et al (2013) A visual-attention model using earth Mover’s distance-based saliency measurement and nonlinear feature combination. IEEE Trans Pattern Anal Mach Intell 35(2):314–328

    Article  Google Scholar 

  40. Liu Z, Xue Y, Yan H, Zhang Z (2011) Efficient saliency detection based on gaussian models. IET Image Process 5(2):122–131

    Article  Google Scholar 

  41. Liu, T. et al., 2007. Learning to Detect a Salient Object. s.l., Proc. IEEE CS Conf. Computer and Vision Pattern Recognition, pp. 1–8.

  42. Loupias, E., Sebe, N., Bres, S. & Jolion, J.M., 2000. Wavelet-based salient points for image retrieval. Vancouver, BC, Canada, s.n., pp. 518–521.

  43. Luo, S. et al., 2013 . Efficient saliency detection using regional color and spatial information. Paris, s.n.

  44. Ma, Y.F. & Zhang, H.J., 2003 . ontrast-based image attention analysis by using fuzzy growing. s.l., s.n.

  45. Mancas, M., 2008. Image Perception: Relative Influence of Bottom–Up and Top-Down Attention. s.l., Proc. Int’l Workshop Attention and Performance in Computer Vision in conjunction with the Int’l Conf Computer Vision Systems, pp. 94–106.

  46. Margolin, R., Tal, A. & Zelnik-Manor, L., 2013. What Makes a Patch Distinct?. s.l., CVPR ’13 Proceedings of the 2013 I.E. Conference on Computer Vision and Pattern Recognition.

  47. Milligan G, Cooper M (1987) Methodology review: clustering methods. Appl Psychol Meas 11(4):329–354

    Article  Google Scholar 

  48. Parkhurst, D., Law, K. & Nieb, E., 2002. Modeling the role of salience in the allocation of overt visual attention. Vision Research, Issue 42, pp. 107–123

  49. Pele, O. & Werman, M., 2008. A Linear Time Histogram Metric for Improved SIFT Matching. Marseille, France, Proceeding ECCV ′08 Proceedings of the 10th European Conference on Computer Vision: Part III, pp. 495–508.

  50. Perazzi F, Krähenbül P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. IEEE CVPR, Rhode Island

    Google Scholar 

  51. Peters R, Iyer A, Itti L, Koch C (2005) Components of bottom–up gaze allocation in natural images. Vision Res 45:2397–2416

    Article  Google Scholar 

  52. Rahmani, R. et al., 2008. Localized Content Based Image Retrieval. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, SPECIAL ISSUE, pp. 1–10

  53. Rai P, Singh S (2010) A survey of clustering techniques. Int J Comput Appl 7(12):1–5

    Google Scholar 

  54. Rosin PL (2009) A simple method for detecting salient regions. Pattern Recognit 42:2363–2371

    Article  MATH  Google Scholar 

  55. Rubner Y, Tomasi C, Guibas L (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40(2):99–121

    Article  MATH  Google Scholar 

  56. Schmid C, Mohr R (1997) Local greyvalue invariants for image retrieval. IEEE Trans Pattern Anal Mach Intell 19(5):530–535

    Article  Google Scholar 

  57. Schmid C, Mohr R, Bauckhage C (2000) Evaluation of interest point detectors. Int J Comput Vis 37(2):151–172

    Article  MATH  Google Scholar 

  58. Sebe N et al (2002) Evaluation of salient point techniques. Image Vision Comput 21:367–377

    Google Scholar 

  59. Song, H., Li, B. & Zhang, L., 2006. Color Salient Points Detection Using Wavelet. Dalian, China, Proceedings of the 6th World Congress on Intelligent Control and Automation.

  60. Stöttinger, J., Hanbury, A.,. & Gevers, T., 2009. Lonely but attractive: Sparse color salient points for object retrieval and categorization. Miami, FL, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009., pp. 1–8

  61. Tan, P.N., Steinbach, M. & Vipin, K., 2006. Cluster Analysis: Basic Concepts and Algorithms. In: Introduction to Data Mining. s.l.:Addison-Wesley Companion, pp. 488–568.

  62. Tatler BW, Hayhoe MM, Land MF, Ballard DH (2011) Eye guidance in natural vision: reinterpreting salience. J Vis 11(11–5):1–23

    Google Scholar 

  63. Tian Q et al (2001) Image retrieval using wavelet-based salient points. J Electron Imaging 10(4):835–849

    Article  Google Scholar 

  64. Toet A (2011) Computational versus psychophysical bottom–Up image saliency: a comparative evaluation study. IEEE Trans Pattern Anal Mach Intell 33(11):2131–2146

    Article  Google Scholar 

  65. Tryfos, P., 1998. Chapter 15: Cluster analysis. . In: Methods for Business Analysis and Forecasting: Text & Cases, . s.l.:Wiley.

  66. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  67. Zhang L (2008) SUN: a bayesian framework for saliency using natural statistics. J Vis 8(7–32):1–20

    Google Scholar 

  68. Zhang, H. & Goldman, S. A., 2006. Image Segmentation Using Salient Points-Based Object Templates.. Atlanta, GA, 2006 I.E. International Conference on Image Processing,, pp. 765–768

  69. Zhao Q, Koch C (2011) Learning a saliency map using fixated locations in natural scenes. J Vis 11(3–9):1–15

    Google Scholar 

  70. Zhou, B., Hou, X. & Zhang, L., 2010. A phase discrepancy analysis of object motion. Queenstown, New Zealand, Proceedings of the 10th Asian conference on Computer vision ACCV’10, pp. 225–238.

Download references

Author information

Authors and Affiliations

  1. Centre for Computational Intelligence, De Montfort University, Leicester, UK

    Mohammad Al-Azawi, Yingjie Yang & Howell Istance

  2. Department of Computer Science, Oman College of Management and Technology, Muscat, Oman

    Mohammad Al-Azawi

Authors
  1. Mohammad Al-Azawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingjie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Howell Istance
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Al-Azawi.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Azawi, M., Yang, Y. & Istance, H. Irregularity-based image regions saliency identification and evaluation. Multimed Tools Appl 75, 25–48 (2016). https://doi.org/10.1007/s11042-014-2248-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2248-z

Keywords

search

Navigation