Automatic Foreground Extraction of Head Shoulder Images

  • Jin Wang
  • Yiting Ying
  • Yanwen Guo
  • Qunsheng Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)


Most existing techniques of foreground extracting work only in interactive mode. This paper introduces a novel algorithm of automatic foreground extraction for special object, and verifies its effectiveness with head shoulder images. The main contribution of our idea is to make the most use of the prior knowledge to constrain the processing of foreground extraction. For human head shoulder images, we first detect face and a few facial features, which helps to estimate an approximate mask covering the interesting region. The algorithm then extracts the hard edge of foreground from the specified area using an iterative graph cut method incorporated with an improved Gaussian Mixture Model. To generate accurate soft edges, a Bayes matting is applied. The whole process is fully automatic. Experimental results demonstrate that our algorithm is both robust and efficient.


Gaussian Mixture Model Face Detection Hard Edge Soft Edge Face Detection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Boykov, Y., Jolly, M.: Interactive Graph cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images. In: IEEE International Conference on Computer Vision, pp. 105–112 (2001)Google Scholar
  2. 2.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut - Interactive Foreground Extraction Using Iterated Graph cuts. In: ACM SIGGRAPH 2004, pp. 309–314 (2004)Google Scholar
  3. 3.
    Li, Y., Sun, J., Tang, C., Shum, H.: Lazy Snapping. In: ACM SIGGRAPH 2004, pp. 303–308 (2004)Google Scholar
  4. 4.
    Kass, M., Witkin, A., Terzolpoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 2, 321–331 (1988)CrossRefGoogle Scholar
  5. 5.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. In: IEEE International Conference on Computer Vision, pp. 694–699 (1995)Google Scholar
  6. 6.
    Mortensen, E., Barrett, W.: Intelligent Scissors for Image Composition. In: ACM SIGGRAPH 1995, pp. 191–198 (1995)Google Scholar
  7. 7.
    COREL Corporation. Knockout user guide (2002)Google Scholar
  8. 8.
    Chuang, Y., Curless, B., Salesin, D., Szeliski, R.: A Bayesian Approach to Digital Matting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271 (2001)Google Scholar
  9. 9.
    Sun, J., Jia, J., Tang, C., Shum, H.: Poisson Matting. In: ACM SIGGRAPH 2004, pp. 315–321 (2004)Google Scholar
  10. 10.
    Vasconcelos, N., Lippman, A.: Embedded Mixture Modeling for Efficient Probabilistic Content-Based Indexing and Retrieval. In: Proc. of SPIE Conf. on Multimedia Storage and Archiving Systems III, Boston (1998)Google Scholar
  11. 11.
    McLachlan, G., Krishnan, T.: The EM Algorithm and Extensions. Wiley Series in Probability and Statistics. John Wiley & Sons, ChichesterGoogle Scholar
  12. 12.
    Lai, Z., Gao, P., Wang, T., et al.: Comparison on Bayesian YING-YANG Theory Based Clustering Number Selection Criterion with Information Theoretical Criteria. In: IEEE International Joint Conference on Neural Networks, Anchorage, USA, vol. 1, pp. 725–729 (1985)Google Scholar
  13. 13.
    Geng, X., Zhong, X.P., Zhou, X.M., Sun, S.P., Zhou, Z.H.: Refining Eye Location Using VPF for Face Detection. In: Proc. of the 3rd Conference of Sinobiometrics, Xi’an China, pp. 25–28 (2002)Google Scholar
  14. 14.
    Mandel, E.D., Penev, P.S.: Facial Feature Tracking and Pose Estimation in Video Sequences by Factorial Coding of the Low-Dimensional Entropy Manifolds due to the Partial Symmetrie s of Faces. In: Proc. 25th IEEE Int’l Conf. Acoustics, Speech and Signal Processing (ICASSP 2000), vol. 4, pp. 2345–2348 (2000)Google Scholar
  15. 15.
    Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  16. 16.
    Chua, T.S., Zhao, Y.L., Kankanhalli, M.S.: Detection of human faces in a compressed domain for video stratification. The Visual Computer 18, 121–133 (2002)zbMATHCrossRefGoogle Scholar
  17. 17.
    Gao, P., Lyu, M.R.: A Study on Color Space Selection for Determining Image Segmentation Region Number. In: Proc. of the 2000 International Conference on Artificial Intelligence, Monte Carlo Resort, Las Vegas, Nevada, USA, June 26-29, vol. 3, pp. 1127–1132 (2000)Google Scholar
  18. 18.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, vol. 1, pp. 511–518 (2001)Google Scholar
  19. 19.
    Ahlberg, J.: Candide-3 – an Updated Parameterized Face. Technical Report LiTH-ISY-R-2326, Linkping University, Sweden (2001)Google Scholar
  20. 20.
    Senior, A., Hsu, R.L., Mottaleb, M.A., Jain, A.: Face Detection in Color Images, vol. 24(5), pp. 696–706. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  21. 21.
    MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proc. of 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  22. 22.
    Orchard, M.T., Bouman, C.A.: Color Quantization of Images. IEEE Transactions on Signal Processing 39(12), 2677–2690 (1991)CrossRefGoogle Scholar
  23. 23.
    Chuang, Y.Y., Agarwala, A., Curless, B., Salesin, D., Szeliski, R.: Video Matting of Complex Scenes. In: ACM SIGGRAPH 2004, vol. 21(3), pp. 243–248 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jin Wang
    • 1
  • Yiting Ying
    • 2
  • Yanwen Guo
    • 2
    • 3
  • Qunsheng Peng
    • 2
  1. 1.Xuzhou Normal UniversityXuzhouChina
  2. 2.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina
  3. 3.School of Computer Science and TechnologyShandong UniversityJinanChina

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