Skip to main content
SpringerLink
Log in

Natural Image Matting with Low-Level Feature Attention Guidance

  • Conference paper
  • First Online:
Knowledge Science, Engineering and Management (KSEM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13370))

Abstract

Previous natural image matting algorithms have difficulties with transition regions in the foreground and background, such as tiny and detailed structures like hair. This paper argues that more efficient low-level features can help the network recover details with minor increases in network capacity and computational complexity. The proposed method, termed low-level feature channel guidance net LFCGN, has two advantages: 1) it introduces a low-level feature channel attention module designed to make the model parameters more efficient and can even lead to high-level feature map generation. 2) a dynamic upsampling is used in the decoder stage, making the detail part recover more efficiently. Experiments are evaluated on the Composition-1k dataset, and the experimental results show that our method obtained competitive performance compared to the state-of-the-art on task of image matting.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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. Cai, S., et al.: Disentangled image matting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8819–8828 (2019)

    Google Scholar 

  2. Chen, Q., Li, D., Tang, C.K.: KNN matting. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2175–2188 (2013)

    Article  Google Scholar 

  3. Chen, Q., Ge, T., Xu, Y., Zhang, Z., Yang, X., Gai, K.: Semantic human matting. In: Proceedings of the 26th ACM international conference on Multimedia, pp. 618–626 (2018)

    Google Scholar 

  4. Chuang, Y.Y., Curless, B., Salesin, D.H., Szeliski, R.: A Bayesian approach to digital matting. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 2, pp. II-II. IEEE (2001)

    Google Scholar 

  5. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  6. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

  7. He, B., Wang, G., Shi, C., Yin, X., Liu, B., Lin, X.: Iterative transductive learning for alpha matting. In: 2013 IEEE International Conference on Image Processing, pp. 4282–4286. IEEE (2013)

    Google Scholar 

  8. He, K., Rhemann, C., Rother, C., Tang, X., Sun, J.: A global sampling method for alpha matting. In: CVPR 2011, pp. 2049–2056. IEEE (2011)

    Google Scholar 

  9. Hou, Q., Liu, F.: Context-aware image matting for simultaneous foreground and alpha estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4130–4139 (2019)

    Google Scholar 

  10. Hu, F., Lakdawala, S., Hao, Q., Qiu, M.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Inf. Technol. Biomed. 13(4), 656–663 (2009)

    Article  Google Scholar 

  11. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  12. Ke, Z., et al.: Is a green screen really necessary for real-time portrait matting? arXiv preprint arXiv:2011.11961 (2020)

  13. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  14. Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2007)

    Article  Google Scholar 

  15. Li, J., Zhang, J., Maybank, S.J., Tao, D.: End-to-end animal image matting. arXiv e-prints pp. arXiv-2010 (2020)

    Google Scholar 

  16. Li, Y., Lu, H.: Natural image matting via guided contextual attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11450–11457 (2020)

    Google Scholar 

  17. Li, Y., Song, Y., Jia, L., Gao, S., Li, Q., Qiu, M.: Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning. IEEE Trans. Ind. Inf. 17(4), 2833–2841 (2020)

    Article  Google Scholar 

  18. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  19. Lu, H., Dai, Y., Shen, C., Xu, S.: Indices matter: learning to index for deep image matting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3266–3275 (2019)

    Google Scholar 

  20. Qiu, H., Zheng, Q., Msahli, M., Memmi, G., Qiu, M., Lu, J.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intell. Transp. Syst. 22(7), 4560–4569 (2020)

    Article  Google Scholar 

  21. Rhemann, C., Rother, C., Wang, J., Gelautz, M., Kohli, P., Rott, P.: A perceptually motivated online benchmark for image matting. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1826–1833. IEEE (2009)

    Google Scholar 

  22. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015 Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  23. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  24. Sengupta, S., Jayaram, V., Curless, B., Seitz, S.M., Kemelmacher-Shlizerman, I.: Background matting: the world is your green screen. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2291–2300 (2020)

    Google Scholar 

  25. Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874–1883 (2016)

    Google Scholar 

  26. Wang, J., Cohen, M.F.: An iterative optimization approach for unified image segmentation and matting. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005) Volume 1, vol. 2, pp. 936–943. IEEE (2005)

    Google Scholar 

  27. Wang, J., Cohen, M.F.: Optimized color sampling for robust matting. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  28. Xu, N., Price, B., Cohen, S., Huang, T.: Deep image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2970–2979 (2017)

    Google Scholar 

  29. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286–301 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, China

    Hang Jiang, Song Wu, Dehong He & Guoqiang Xiao

Authors
  1. Hang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Song Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dehong He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guoqiang Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guoqiang Xiao .

Editor information

Editors and Affiliations

  1. Télécom Paris, Paris, France

    Gerard Memmi

  2. Purdue University, West Lafayette, IN, USA

    Baijian Yang

  3. Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Linghe Kong

  4. Nanyang Technological University, Singapore, Singapore

    Tianwei Zhang

  5. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Jiang, H., Wu, S., He, D., Xiao, G. (2022). Natural Image Matting with Low-Level Feature Attention Guidance. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10989-8_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10988-1

  • Online ISBN: 978-3-031-10989-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation