Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Information Processing

IIP 2020: Intelligent Information Processing X pp 3–13Cite as

A Salient Object Detection Algorithm Based on Region Merging and Clustering

  • Conference paper
  • First Online:

  • 563 Accesses

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 581))

Abstract

Salient object detection has recently drawn much attention in computer vision such as image compression and object tracking. Currently, various heuristic computational models have been designed. However, extracting the salient objects with a complex background in the image is still a challenging problem. In this paper, we propose a region merging strategy to extract salient region. Firstly, boundary super-pixels are clustered to generate the initial saliency maps based on the prior knowledge that the image boundaries are mostly background. Next, adjacent regions are merged by sorting the multiple feature values of each region. Finally, we get the final saliency maps by merging adjacent or non-adjacent regions by means of the distance from the region to the image center and the boundary length of overlapping regions. The experiments demonstrate that our method performs favorably on three datasets than state-of-art.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   54.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Cheng, M.M., Mitra, N.J., Huang, X., et al.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)

    Article  Google Scholar 

  2. Itti, L.: Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans. Image Process. 13(10), 1304–1318 (2004)

    Article  Google Scholar 

  3. Wang, X.F., Qi, C.: A behavior recognition method using salient object detection. J. Xi’an Jiaotong Univ. (2018)

    Google Scholar 

  4. Li, R., Li, J.P., Song, C.: Research on co-segmentation of image based on salient object detection. Modern Comput. (16), 19–23 (2017)

    Google Scholar 

  5. Sun, F., Qing, K.H., Sun, W., et al.: Image saliency detection based on region merging. J. Comput. Aided Des. Graph. 28(10), 1679–1687 (2016)

    Google Scholar 

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

    Google Scholar 

  7. Peng, H., Li, B., Ling, H., et al.: Salient object detection via structured matrix decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 818–832 (2017)

    Article  Google Scholar 

  8. Zhang, D., Fu, H., Han, J., et al.: A review of co-saliency detection algorithms: fundamentals, applications, and challenges. ACM Trans. Intell. Syst. Technol. (TIST) 9(4), 38 (2018)

    Google Scholar 

  9. Yazdi, M., Bouwmans, T.: New trends on moving object detection in video images captured by a moving camera: a survey. Comput. Sci. Rev. 28, 157–177 (2018)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  11. Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  12. Achanta, R., Hemami, S., Estrada, F., et al.: Frequency-tuned salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)

    Google Scholar 

  13. Zhang, Q., Lin, J., Li, W., Shi, Y., Cao, G.: Salient object detection via compactness and objectness cues. Vis. Comput. 34(4), 473–489 (2017). https://doi.org/10.1007/s00371-017-1354-0

    Article  Google Scholar 

  14. Murray, N., Vanrell, M., Otazu, X., et al.: Saliency estimation using a non-parametric low-level vision model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 433–440 (2011)

    Google Scholar 

  15. Achanta, R., Shaji, A., Smith, K., et al.: Slic superpixels. EPFL, Technical report 149300, November 2010

    Google Scholar 

  16. Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_27

    Chapter  Google Scholar 

  17. Seo, H.J., Milanfar, P.: Static and space-time visual saliency detection by self-resemblance. J. Vis. 9(12), 1–27 (2009)

    Article  Google Scholar 

  18. Hou, X., Harel, J., Koch, C.: Image signature: highlighting sparse salient regions. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 194–201 (2012)

    Article  Google Scholar 

  19. Rezazadegan Tavakoli, H., Rahtu, E., Heikkilä, J.: Fast and efficient saliency detection using sparse sampling and kernel density estimation. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 666–675. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_62

    Chapter  Google Scholar 

  20. Feng, L., Wen, P., Miao, Y., et al.: An image saliency detection algorithm based on color and space information. In: 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE (2017)

    Google Scholar 

  21. Xu, Q., Wang, F., Gong, Y., et al.: An edge-oriented framework for saliency detection. In: 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE (2017)

    Google Scholar 

  22. Li, G., Yu, Y.: Visual saliency detection based on multiscale deep CNN features. IEEE Trans. Image Process. 25, 5012–5024 (2016)

    Article  MathSciNet  Google Scholar 

  23. Li, H., Chen, J., Lu, H., et al.: CNN for saliency detection with low-level feature integration. Neurocomputing 226, 212–220 (2017)

    Article  Google Scholar 

  24. 赵恒, 安维胜, 田怀文. 结合稀疏重构与能量方程优化的显著性检测. 计算机应用研究 (6) (2019)

    Google Scholar 

  25. 余映, 吴青龙, 邵凯旋, et al.: 超复数域小波变换的显著性检测. 电子与信息学报 41(9) (2019)

    Google Scholar 

  26. Guo, Y., Liu, Y., Ma, R.: Image saliency detection based on geodesic-like and boundary contrast maps. ETRI J. 41(6), 797–810 (2019)

    Article  Google Scholar 

  27. Marcon, M., Spezialetti, R., Salti, S., Silva, L., Di Stefano, L.: Boosting object recognition in point clouds by saliency detection. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11808, pp. 321–331. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30754-7_32

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu, China

    Weiyi Wei, Yijing Yang, Wanru Wang, Xiufeng Zhao & Huifang Ma

Authors
  1. Weiyi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yijing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wanru Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiufeng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huifang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yijing Yang .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Salford, Manchester, UK

    Sunil Vadera

  3. Australian Defence Force Academy, UNSW Canberra, Canberra, ACT, Australia

    Elizabeth Chang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wei, W., Yang, Y., Wang, W., Zhao, X., Ma, H. (2020). A Salient Object Detection Algorithm Based on Region Merging and Clustering. In: Shi, Z., Vadera, S., Chang, E. (eds) Intelligent Information Processing X. IIP 2020. IFIP Advances in Information and Communication Technology, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-030-46931-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-46931-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46930-6

  • Online ISBN: 978-3-030-46931-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation