Skip to main content
SpringerLink
Log in

Saliency threshold: a novel saliency detection model using Ising’s theory on Ferromagnetism (STIF)

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

With the advancement of time, the computer vision systeams are focusing on mimicking the human visual system. In this manuscript, we tried to develop a model which works at improving both the detection accuracy and computation time. First, two double opponent color based features and twelve directional edge features using Gabor filter are computed. Then the most dominant feature pertaining to the salient object is extracted using principal component analysis to form the saliency map. Further, a threshold is applied on the saliency map to detect the salient object present in the image. This threshold selection is a vital procedure. We mapped the Ising model of ferromagnetism to the salient object detection problem by employing an optimization problem for this threshold selection. Experimental results show that the proposed model outperforms the existing models in terms of detection accuracy and also takes less computation time in comparison to many methods.

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Borji, A., Cheng, M., Jiang, H., Li, J.: Salient object detection: a survey. arXiv 1411, 5878 (2014)

    Google Scholar 

  3. Li, Z., Itti, L.: Saliency and gist features for target detection in satellite images. IEEE Trans. Image Process. 20, 2017–2029 (2011)

    Article  MathSciNet  Google Scholar 

  4. Graefe, R., Efenberger, W.: A novel approach for the detection of vehicles on freeways by real time vision. Intell. Veh. 1996, 363–368 (1996)

    Article  Google Scholar 

  5. Itti, L.: Models of bottom up and top down visual attention. California Institute of Technology, Pasadena (2000)

    Google Scholar 

  6. Karssemeijer, N.: Detection of stellate distortions in mammograms. IEEE Trans. Med. Imaging 15, 611–619 (2006)

    Article  Google Scholar 

  7. Santella, A., Agrawala, M., Decarlo, D., Salesin, D., Cohen, M.: Gaze based interaction for semi-automatic photo cropping. In: Proceedings of Conference Human Factors in Computing Systems pp. 771–780 (2006)

  8. Rother, C., Bordeaux, L., Hamadi, Y., Blake, A.: Autocollage. ACM Siggraph 25, 847–852 (2006)

    Article  Google Scholar 

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

    Google Scholar 

  10. Bruce, N.D.B., Tsotsos, J.K.: Saliency Based on Information Maximization. Adv. Neural Inf. Process. Syst. 18, 155–162 (2006)

    Google Scholar 

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

    Article  Google Scholar 

  12. Meur, O.L., Callet, P.L., Barba, D., Thoreau, D.: A coherent computational approach to model bottom up visual attention. IEEE Trans. Pattern Anal. Mach. Intell. 28, 802–817 (2006)

    Article  Google Scholar 

  13. Harel, J., Koch, C., Perona, P. (2007)Graph based visual saliency. In:Proceedings of the Advances in Neural Information and Processing Systems pp. 545–552 (2007)

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

  15. Yu, Z., Wong, H.S.: A rule based technique for extraction of visual attention regions based on real time clustering. IEEE Trans. Multimed. 9, 766–784 (2007)

    Article  Google Scholar 

  16. 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, 1–20 (2008)

    Google Scholar 

  17. Liu, T., Yuan, Z., Sun-Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to Detect A Salient Object. IEEE Trans. Pattern Anal. Mach. Intell. 33, 353–366 (2011)

    Article  Google Scholar 

  18. Achanta, R., Hemamiz, S., Estraday, F., Susstrunk S.: Frequency-tuned salient region detection.In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition pp. 1597–1604 (2004)

  19. Achanta, R., Susstrunk S.: Saliency detection using maximum symmetric surround. In: Proceedings in International Conference on Image Processing pp. 2653–2656 (2010)

  20. Zhang, W., Wu, Q.M.J., Wang, G., Yin, H.: An Adaptive Computational Model for Salient Object Detection. IEEE Trans. Multimed. 12, 300–315 (2010)

    Article  Google Scholar 

  21. Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1915–1926 (2012)

    Article  Google Scholar 

  22. Liu, Z., Shi, R., Shen, L., Xue, Y., Ngan, K.N., Zhang, Z.: Unsupervised salient object segmentation based on kernel density estimation and two-phase graph cut. IEEE Trans. Multimed. 14, 1275–1289 (2012)

    Article  Google Scholar 

  23. Shen, X., Wu Y.: A unified approach to salient object detection via low rank matrix recovery. In: Proceedings in IEEE Conference on Computer Vision and Pattern Recognition pp. 853–860 (2012)

  24. Vikram, T.N., Tscherepanow, M., Wrede, B.: A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recogn. 45, 3114–3124 (2012)

    Article  Google Scholar 

  25. İmamoğlu, N., Lin, W., Fang, Y.: A saliency detection model using low-level features based on wavelet transform. IEEE Trans. Multimedia 15, 96–105 (2013)

    Article  Google Scholar 

  26. Singh, N., Agrawal, R.K.: Combination of Kullback-Leibler divergence and Manhattan distance measures to detect salient objects. SIVIP (2013). https://doi.org/10.1007/s11760-013-0457-y

    Article  Google Scholar 

  27. Singh, N., Arya, R., Agrawal, R.K.: A novel approach to combine features for salient object detection using constrained particle swarm optimization. Pattern Recogn. 47, 1731–1739 (2014)

    Article  Google Scholar 

  28. Liu, Z., Zou, W., Meur, O.L.: Saliency tree: a novel saliency detection framework. IEEE Trans. Image Process. 23, 1937–1952 (2014)

    Article  MathSciNet  Google Scholar 

  29. Zhu, L., Klein, D.A., Frintrop, S., Cao, Z., Cremers, A.B.: A multisize super pixel approach for salient object detection based on multivariate normal distribution estimation. IEEE Trans. Image Process. 23, 5094–5107 (2014)

    Article  MathSciNet  Google Scholar 

  30. Peng, P., Shao, L., Han, J., Han, J.: Saliency-aware image-to-class distances for image classification. Neurocomputing 166, 337–345 (2015)

    Article  Google Scholar 

  31. Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., Li, S.: Salient object detection: a discriminative regional feature integration approach. In Proceedings of the IEEE conference on computer vision and pattern recognition pp. 2083–2090 (2013)

  32. Fu, H, Cao X., Tu Z.: Cluster-Based Co-Saliency Detection. IEEE Transactions on Image Processing (2013)

  33. Zhao, R., Wanli, O., Hongsheng, L., Xiaogang, W.: Saliency detection by multi-context deep learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265–1274 (2015)

  34. Lin, Y., Kong, S., Wang, D. and Zhuang, Y.: Saliency detection within a deep convolutional architecture. In Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)

  35. Zhang, D., Han, J., Han, J., Shao, L.: Cosaliency detection based on intrasaliency prior transfer and deep intersaliency mining. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1163–1176 (2016)

    Article  MathSciNet  Google Scholar 

  36. Li, G. and Yu, Y.: Deep contrast learning for salient object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 478–487 (2016)

  37. Xie, Y., Lu, H., Yang, M.-H.: Bayesian saliency via low and mid level cues. IEEE Trans. Image Process. 22, 1689–1698 (2013)

    Article  MathSciNet  Google Scholar 

  38. Qin, Y., Lu, H., Xu Y., Wang H.E.: Saliency detection via cellular automata. In: IEEE Conference on Computer Vision and Pattern Recognition pp.110–119 (2015)

  39. Singh, N., Arya, R., Agrawal, R.K.: A novel position prior using fusion of rule of thirds and image center for salient object detection. Multimed. Tools Appl.d 76(8), 10521–10538 (2017)

    Article  Google Scholar 

  40. Singh, N., Arya, R., Agrawal, R.K.: A convex hull approach in conjunction with Gaussian mixture model for salient object detection. Digit. Signal Process. 55, 22–31 (2016)

    Article  Google Scholar 

  41. Singh, N., Arya, R., Agrawal, R.K.: Performance enhancement of salient object detection using super pixel based Gaussian mixture model. Multimed. Tools Appl. 77, 1–19 (2017)

    Google Scholar 

  42. Singh, N., Mishra, K.K., Bhatia, S.: SEAM an improved environmental adaptation method with real parameter coding for salient object detection. Multimed. Tools Appl. (2020). https://doi.org/10.1007/s11042-020-08678-z

    Article  Google Scholar 

  43. Chen, S., Tan, X., Wang, B., Huchuan, Lu, Xuelong, Hu, Yun, Fu: Reverse attention for salient object detection. IEEE Trans. Image Process. 29, 3763–3776 (2020)

    Article  Google Scholar 

  44. Zhang, L., Wu, J., Wang, T., Borji, A., Wei, G., & Lu, H.: A Multistage Refinement Network for Salient Object Detection. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society (2020)

  45. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)

    Article  Google Scholar 

  46. Ising, E.: Beitrag zur theorie des ferromagnetismus. Zeitschrift für Physik A Hadrons Nucl. 31(1), 253–258 (1925)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, India

    Navjot Singh

Authors
  1. Navjot Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Navjot Singh.

Additional information

Communicated by Y. Zhang.

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

Singh, N. Saliency threshold: a novel saliency detection model using Ising’s theory on Ferromagnetism (STIF). Multimedia Systems 26, 397–411 (2020). https://doi.org/10.1007/s00530-020-00650-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-020-00650-z

Keywords

Search

Navigation