Advertisement

Attributed Graph Matching Based Engineering Drawings Retrieval

  • Rujie Liu
  • Takayuki Baba
  • Daiki Masumoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3163)

Abstract

This paper presents a method for engineering drawings retrieval by their shape appearances. In this method, an engineering drawing is represented by an attributed graph, where each node corresponds to a meaningful primitive extracted from the original drawing image. This representation, which characterizes the primitives as well as their spatial relationships by graph nodes attributes and edges attributes respectively, provides a global vision of the drawings. Thus, the retrieval problem can be formulated as one of attributed graph matching, which is realized by mean field theory in this paper. The effectiveness of this method is verified by experiments.

Keywords

Engineering drawings retrieval Attributed graph Mean field theory 

References

  1. 1.
    Tombre, K.: Analysis of Engineering Drawings: State of the Art and Challenges, Tombre, K., Chhabra, A.K., Graphics Recognition – Algorithms and Systems, 257–264Google Scholar
  2. 2.
    Kanungo, T., Haralick, R.M., Dori, D.: Understanding Engineering Drawings: A Survey. In: International Workshop on Graphics Recognition, pp. 119–130 (1995)Google Scholar
  3. 3.
    Kasturi, R., Bow, S.T., El-Masri, W., et al.: A System for Interpretation of Line Drawings. IEEE Trans. On P.A.M.I. 12(10), 978–992 (1990)Google Scholar
  4. 4.
    Dori, D., Liu, W.: Sparse Pixel Vectorization: an algorithm and its Performance Evaluation. IEEE Trans. on P.A.M.I. 21(3), 202–215 (1999)Google Scholar
  5. 5.
    Song, J., Su, F., et al.: An Object-Oriented Progressive-Simplification-Based Vectorization System for Engineering Drawings: Model, Algorithm, and Performance. IEEE Trans. on P.A.M.I. 24(8), 1048–1060 (2002)Google Scholar
  6. 6.
    Huet, B., Hancock, E.R.: Relational Object Recognition from Large Structural Libraries. Pattern Recognition 35, 1895–1915 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Christmas, W.J., Kittler, J., Petrou, M.: Structural Matching in Computation Vision using Probabilistic Relaxation. IEEE Trans. on P. A. M. I. 17(8), 749–764 (1995)Google Scholar
  8. 8.
    Gold, S., Rangarajan, A.: A Graduated Assignment Algorithm for Graph Matching. IEEE Trans. on P. A. M. I. 18, 377–388 (1996)Google Scholar
  9. 9.
    Li, S.Z., Wang, H., Chan, K.L.: Energy Minimization and Relaxation Labeling. Journal of Mathematical Imaging and Vision 7, 149–161 (1997)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Wyk, M.A., Durrani, T.S., Wyk, B.J.: A RKHS Interpolator-Based Graph Matching Algorithm. IEEE Trans. on P. A. M. I. 24(7), 988–995 (2002)Google Scholar
  11. 11.
    Cross, A.D.J., Wilson, R.C., Hancock, E.R.: Inexact Graph Matching using Genetic Search. Pattern Recognition 30(6), 953–970 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Rujie Liu
    • 1
  • Takayuki Baba
    • 2
  • Daiki Masumoto
    • 2
  1. 1.Fujitsu Research and Development Center Co. LTDBeijingP.R.China
  2. 2.Information Technology Media LabsFujitsu Laboratories LTDKawasakiJapan

Personalised recommendations