Advertisement

Online Composite Sketchy Shape Recognition Based on Bayesian Networks

  • Zhengxing Sun
  • Lisha Zhang
  • Bin Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)

Abstract

This paper presents a novel approach for online multi-strokes composite sketchy shape recognition based on Bayesian Networks. By means of the definition of a double-level Bayesian networks, a classifier is designed to model the intrinsic temporal orders among the strokes effectively, where a sketchy shape is modeled with the relationships not only between a stroke and its neighbouring strokes, but also between a stroke and all of its subsequence.. The drawing-style tree is then adopted to capture the users’ accustomed drawing styles and simplify the training and recognition of Bayesian network classifier. The experiments prove both effectiveness and efficiency of the proposed method.

Keywords

Support Vector Machine Feature Vector Bayesian Network Support Vector Machine Classifier Shape Class 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sun, Z., Liu, J.: Informal user interface for graphical computing. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 675–682. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Landay, J.A., Myers, B.A.: Sketching Interfaces: toward more human interface design. IEEE Computer 34(3), 56–64 (2001)Google Scholar
  3. 3.
    Newman, M.W., James, L., Hong, J.I., et al.: DENIM: An informal web site design tool inspired by observations of practice. In: HCI, vol. 18, pp. 259–324 (2003)Google Scholar
  4. 4.
    Fonseca, M.J., Pimentel, C., Jorge, J.A.: CALI - an online scribble recognizer for calligraphic interfaces. In: AAAI Spring Symposium on Sketch Understanding, pp. 51–58. AAAI Press, Menlo Park (2002)Google Scholar
  5. 5.
    Calhoun, C., Thomas, F.S., Kurtoglu, T., et al.: Recognizing multi-stroke symbols. In: AAAI Spring Symposium on Sketch Understanding, pp. 15–23. AAAI Press, Menlo Park (2002)Google Scholar
  6. 6.
    Xu, X., Sun, Z., et al.: An online composite graphics recognition approach based on matching of spatial relation graphs. IIDAR 7(1), 44–55 (2004)MathSciNetGoogle Scholar
  7. 7.
    Sun, Z., Liu, W., Peng, B., et al.: User adaptation for online sketchy shape recognition. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 305–316. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Sun, Z., Zhang, L., Tang, E.: An incremental learning algorithm based on SVM for online sketchy shape recognition. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 655–659. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Sezgin, T.M., Davis, R.: HMM-Based Efficient Sketch Recognition. In: Proceedings of the 10th international conference on IUI, San Diego, California, USA (January 2005)Google Scholar
  10. 10.
    Sun, Z., Jiang, W., Sun, J.: Adaptive Online Multi-Stroke Sketch Recognition based on Hidden Markov Model. LNCS (LNAI), vol. 3784, pp. 948–957. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. In: Machine learning, vol. 29(2-3), pp. 131–163. Kluwer Academic Publishers, Hingham (1997)Google Scholar
  12. 12.
    Sung-Jung, C., Kim Jin, H.: Bayesian network modeling of Hangul characters for online handwriting recognition. In: Proceedings of IDAR 2003, pp. 207–211 (2003)Google Scholar
  13. 13.
    Alvarado, C., Davis, R.: Dynamically Constructed Bayesian Networks for Sketch Understanding. In: Proceedings of IJCAI 2005, Edinburgh, Scotland (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhengxing Sun
    • 1
  • Lisha Zhang
    • 1
  • Bin Zhang
    • 1
  1. 1.State Key Lab for Novel Software TechnologyNanjing UniversityPR China

Personalised recommendations