Advertisement

Alpha Matting Using Artificial Immune Network

  • Zhifeng Hao
  • Jianming Liu
  • Xueming Yan
  • Wen Wen
  • Ruichu Cai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

Alpha matting refers to the problem of softly extracting the foreground from an image.To solve the matting problem initialized with a trimap (a partition of the image into three regions: foreground, background and unknown pixels), an approach based on artificial immune network is proposed in this paper.The method firstly uses Artificial Immune Network(aiNet) to map the color feature for unknown region, attaining the color subset both on the foreground and background color distributions,then estimate the alpha matte for unknown region, and finally apply guided filter to improve the matting results. Experiments on several different image data sets show that the proposed method produces high-quality matting results.

Keywords

alpha matting Artificial Immune Network feature map 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Porter, T., Duff, T.: Compositing digital images. In: 11th Annual Conference on Computer Graphics and Interactive Techniques, pp. 253–259. ACM Press, New York (1984)Google Scholar
  2. 2.
  3. 3.
    Jue, W., Cohen, M.F.: Image and Video Matting: A Survey. Computer Graphics and Vision 3 (2007)Google Scholar
  4. 4.
    Ruzon, M.A., Tomasi, C.: Alpha estimation in natural images. In: Computer Vision and Pattern Recognition, pp. 18–25. IEEE Press, New York (2000)Google Scholar
  5. 5.
    Chuang, Y., Curless, B., Salesin, D., Szeliski, R.: A bayesian approach to digital matting. In: Computer Vision and Pattern Recognition, pp. 264–271. IEEE Press, New York (2001)Google Scholar
  6. 6.
    Rhemann, C., Rother, C., Gelautz, M.: Improving color modeling for alpha matting. In: BMVC (2008)Google Scholar
  7. 7.
    Wang, J., Cohen, M.: Optimized color sampling for robust matting. In: Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press, New York (2007)Google Scholar
  8. 8.
    Gastal, E.S.L., Oliveira, M.M.: Shared sampling for real-time alpha matting. Computer Graphics Forum, 575–584 (2010)Google Scholar
  9. 9.
    Bai, X., Sapiro, G.: A geodesic framework for fast interactive image and video segmentation and matting. In: 10th IEEE International Conference on Computer Vision, pp. 1–8. IEEE Press, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut - interactive foreground extraction using iterated graph cut. In: Proceedings of ACM SIGGRAPH, pp. 309–314. ACM Press, New York (2004)Google Scholar
  11. 11.
    Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. In: Proceedings of ACM SIGGRAPH, pp. 315–321. ACM Press, New York (2004)Google Scholar
  12. 12.
    Zheng, Y., Kambhamettu, C., Yu, J., Bauer, T., Steiner, K.: Fuzzymatte:A computationally efficient scheme for interactive matting. In: Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press, New York (2008)Google Scholar
  13. 13.
    Levin, A., Lischinski, D., Weiss, Y.: A closed form solution to natural image matting. In: Pattern Analysis and Machine Intelligence, pp. 228–242. IEEE Press, New York (2008)Google Scholar
  14. 14.
    Levin, A., Ravacha, A., Lischinski, D.: Spectral matting. In: Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press, New York (2007)Google Scholar
  15. 15.
    Wang, J., Cohen, M.: Optimized color sampling for robust matting. In: Computer Vision and Pattern Recognition, pp. 1–8. IEEE Press, New York (2007)Google Scholar
  16. 16.
    He, K., Sun, J., Tang, X.: Guided Image Filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Hart, E., Timmis, J.: Application areas of AIS: the past, the present and the future. Applied Soft Computing, 191–201 (2008)Google Scholar
  18. 18.
    Castro, L.N., Timmis, J.I.: Artificial immune systems as a novel soft computing paradigm. Soft Computing, 526–544 (2003)Google Scholar
  19. 19.
    Ge, H., Yan, X.: A Modified Artificial Immune Network for Feature Extracting. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 408–415. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhifeng Hao
    • 1
  • Jianming Liu
    • 1
  • Xueming Yan
    • 2
  • Wen Wen
    • 1
  • Ruichu Cai
    • 1
    • 3
  1. 1.Faculty of Computer ScienceGuangdong University of TechnologyGuangzhouChina
  2. 2.School of Computer ScienceSouth China Normal UniversityGuangzhouChina
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

Personalised recommendations