Skip to main content
SpringerLink
Log in

Salient Object Detection Using Spatially Weighted Multiple Contrast Cues

  • Conference paper
  • First Online:
Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

  • 1650 Accesses

Abstract

Detecting objects that capture visual attention has played a key role in computer vision. In this paper, we present a model that incorporates multiple bottom-up cues depending on the following concepts: connectivity to the outstanding parts where each superpixel will take a saliency score based on its connectivity strength to the enclosed interest regions, global contrast by measuring how every superpixel differs from all superpixels in the image and utilizing regional frequency tuning and center-bias. The final saliency map is produced by integrating the resulted foreground maps of each cue and refining the result with the optimization framework. An extensive experimental evaluation is done on three challenging datasets to evaluate the proposed model using common classification criteria. Our model has the superiority in performance over the other models qualitatively and quantitatively.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

    Article  Google Scholar 

  2. Nguyen, T.V., Zhao, Q., Yan, S.: Attentive Systems: A Survey. Int. J. Comput. Vis. 126, 86–110 (2017)

    Article  Google Scholar 

  3. Borji, A., Cheng, M.-M., Hou, Q., Jiang, H., Li, J.: Salient object detection: a survey. Comput. Vis. Media 5, 117–150 (2019)

    Article  Google Scholar 

  4. 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), 1254–1259 (1998)

    Article  Google Scholar 

  5. Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami (2009)

    Google Scholar 

  6. Perazzi, F., Krahenbuhl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  7. Wei, Y., Wen, F., Zhu, W., Sun, J.: Geodesic saliency using background priors. In: Computer Vision – ECCV 2012, pp. 29–42. Springer Berlin Heidelberg (2012)

    Google Scholar 

  8. Fu, K., Gong, C., Gu, I.Y.H., Yang, J.: Geodesic saliency propagation for image salient region detection. In: 2013 IEEE International Conference on Image Processing (2013)

    Google Scholar 

  9. Yang, J., Yang, M.-H.: Top-down visual saliency via joint CRF and dictionary learning. IEEE Trans. Pattern Anal. Mach. Intell. 39(3), 576–588 (2017)

    Article  Google Scholar 

  10. Liu, T., Sun, J., Zheng, N.N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007 CVPR (2007)

    Google Scholar 

  11. Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: 2010 IEEE International Conference on Image Processing (2010)

    Google Scholar 

  12. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007 (2007)

    Google Scholar 

  13. 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), 9–23 (2016)

    Article  MathSciNet  Google Scholar 

  14. Zhang, L., Gu, Z., Li, H.: SDSP: A novel saliency detection method by combining simple priors. In: 2013 IEEE International Conference on Image Processing (2013)

    Google Scholar 

  15. Tong, N., Lu, H., Zhang, L., Ruan, X.: Saliency detection with multi-scale superpixels. IEEE Sig. Process. Lett. 21(9), 1035–1039 (2014)

    Article  Google Scholar 

  16. Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  17. Srivatsa, R.S., Babu, R.V.: Salient object detection via objectness measure. In: 2015 IEEE International Conference on Image Processing (ICIP) (2015)

    Google Scholar 

  18. Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  19. Weijer, J., Gevers, T., Bagdanov, A.D.: Boosting color saliency in image feature detection. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 150–156 (2006)

    Article  Google Scholar 

  20. Xie, Y., Lu, H.: Visual saliency detection based on Bayesian model. In: 2011 18th IEEE International Conference on Image Processing (2011)

    Google Scholar 

  21. Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. 4(12), 2379–2394 (1987)

    Article  Google Scholar 

  22. Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended CSSD. IEEE Trans. Pattern Anal. Mach. Intell. 38(4), 717–729 (2016)

    Article  Google Scholar 

  23. Liu, G.-H., Yang, J.-Y.: Exploiting color volume and color difference for salient region detection. IEEE Trans. Image Process. 28(1), 6–16 (2019)

    Article  MathSciNet  Google Scholar 

  24. 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 (2013)

    Google Scholar 

  25. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Computer Vision – ECCV 2014, Springer International Publishing, pp. 391–405 (2014)

    Google Scholar 

  26. Zhao, R., Ouyang, W., Li, H., Wang, X.: Saliency detection by multi-context deep learning. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

  27. Li, G., Yu, Y.: Visual saliency based on multiscale deep features. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Informatics, Computer Science Department, Suez Canal University, Ismailia, Egypt

    Norhan M. Saleh, Mohamed Tahoun, Abd El Rahman Shabayek & M.-H. Mousa

Authors
  1. Norhan M. Saleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Tahoun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abd El Rahman Shabayek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M.-H. Mousa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Norhan M. Saleh .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saleh, N.M., Tahoun, M., Shabayek, A.E.R., Mousa, MH. (2021). Salient Object Detection Using Spatially Weighted Multiple Contrast Cues. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_73

Download citation

Publish with us

Policies and ethics

Search

Navigation