Advertisement

A Novel Image Matting Approach Based on Naive Bayes Classifier

  • Zhanpeng Zhang
  • Qingsong Zhu
  • Yaoqin Xie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

Image matting is a fundamental technique used in many image and video applications. It aims to softly extract foreground from the image accurately. In this paper, we propose a new matting approach based on naive Bayes classifier to produce matting results with higher accuracy. Spatially-varying probabilistic models for the classifier are established. Confidence values are defined to make better use of the classification results. The results are then refined and combined with closed-form matting to obtain the final alpha matte. We conduct qualitative and quantitative evaluations. Results show that our method outperforms many recent algorithms.

Keywords

image matting naive bayes classifier foreground extraction image segmentation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chuang, Y.Y., Curless, B., Salesin, D.H., et al.: A Bayesian Approach to Digital Matting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 264–271 (2001)Google Scholar
  2. 2.
    Anat, L., Dani, L., Yair, W.: A Closed Form Solution to Natural Image Matting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 61–68 (2006)Google Scholar
  3. 3.
    Wang, J., Cohen, M.F.: Optimized Color Sampling for Robust Matting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17–22 (2007)Google Scholar
  4. 4.
    Rhemann, C., Rother, C., Gelautz, M.: Improving Color Modeling for Alpha Matting. In: Proceedings of British Machine Vision Conference, pp. 1155–1164 (2008)Google Scholar
  5. 5.
    Gastal, E.S.L., Oliveira, M.M.: Shared Sampling for Real-time Alpha Matting. Computer Graphics Forum 29, 575–584 (2010)CrossRefGoogle Scholar
  6. 6.
    Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. ACM Transactions on Graphics 23(3), 315–321 (2004)CrossRefGoogle Scholar
  7. 7.
    He, K.M., Sun, J., Tang, X.O.: Fast Matting Using Large Kernel Matting Laplacian Matrices. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2165–2172 (2010)Google Scholar
  8. 8.
    Pedro, D., Michael, P.: On the Optimality of the Simple Bayesian Classifier under Zero-one Loss. Machine Learning 29, 103–130 (1997)zbMATHCrossRefGoogle Scholar
  9. 9.
    Jolliffe, I.T.: Principal Component Analysis. Springer (1986)Google Scholar
  10. 10.
    Zheng, Y.J., Kambhamettu, C.: Learning Based Digital Matting. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), pp. 889–896 (2009)Google Scholar
  11. 11.
    Rhemann, C., Rother, C., Wang, J., et al.: A Perceptually Motivated Online Benchmark for Image Matting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1826–1833 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhanpeng Zhang
    • 1
    • 2
  • Qingsong Zhu
    • 1
  • Yaoqin Xie
    • 1
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.Sun Yat-Sen UniversityGuangzhouChina

Personalised recommendations