A Bag-of-Paths Based Serialized Subgraph Matching for Symbol Spotting in Line Drawings

  • Anjan Dutta
  • Josep Lladós
  • Umapada Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)


In this paper we propose an error tolerant subgraph matching algorithm based on bag-of-paths for solving the problem of symbol spotting in line drawings. Bag-of-paths is a factorized representation of graphs where the factorization is done by considering all the acyclic paths between each pair of connected nodes. Similar paths within the whole collection of documents are clustered and organized in a lookup table for efficient indexing. The lookup table contains the index key of each cluster and the corresponding list of locations as a single entry. The mean path of each of the clusters serves as the index key for each table entry. The spotting method is then formulated by a spatial voting scheme to the list of locations of the paths that are decided in terms of search of similar paths that compose the query symbol. Efficient indexing of common substructures helps to reduce the computational burden of usual graph based methods. The proposed method can also be seen as a way to serialize graphs which allows to reduce the complexity of the subgraph isomorphism. We have encoded the paths in terms of both attributed strings and turning functions, and presented a comparative results between them within the symbol spotting framework. Experimentations for matching different shape silhouettes are also reported and the method has been proved to work in noisy environment also.


Symbol spotting Serialization of graphs Graph matching Bag-of-paths Attributed strings Turning function Graphical indexing Mean paths 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arkin, E.M., Paul Chew, L., Huttenlocher, D.P., Kedem, K., Mitchell, J.S.B.: An efficiently computable metric for comparing polygonal shapes. IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 209–216 (1991)CrossRefzbMATHGoogle Scholar
  2. 2.
    Delalandre, M., Pridmore, T., Valveny, E., Locteau, H., Trupin, E.: Building synthetic graphical documents for performance evaluation, pp. 288–298. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    Dutta, A.: Symbol spotting in graphical documents by serialized subgraph matching, Master’s thesis, Computer Vision Centre, Universitat Autònoma de Barcelona, Edifici O, Campus UAB, 08193 Bellatera, Barcelona, Spain (September 2010)Google Scholar
  4. 4.
    Lladós, J., Martí, E., Villanueva, J.J.: Symbol recognition by error-tolerant subgraph matching between region adjacency graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1137–1143 (2001)CrossRefGoogle Scholar
  5. 5.
    Messmer, B.T., Bunke, H.: A new algorithm for error-tolerant subgraph isomorphism detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 493–504 (1998)CrossRefGoogle Scholar
  6. 6.
    Müller, S., Rigoll, G.: Engineering drawing database retrieval using statistical pattern spotting techniques. In: Chhabra, A.K., Dori, D. (eds.) GREC 1999. LNCS, vol. 1941, pp. 246–255. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Nayef, N., Breuel, T.M.: A branch and bound algorithm for graphical symbol recognition in document images. In: Proceedings of Ninth IAPR International Workshop on Document Analysis System (DAS 2010), pp. 543–546 (2010)Google Scholar
  8. 8.
    Rusiñol, M., Borràs, A., Lladós, J.: Relational indexing of vectorial primitives for symbol spotting in line-drawing images. Pattern Recognition Letters 31(3), 188–201 (2010)CrossRefGoogle Scholar
  9. 9.
    Rusiñol, M., Lladós, J., Sánchez, G.: Symbol spotting in vectorized technical drawings through a lookup table of region strings. Pattern Analysis and Applications 13, 1–11 (2009)Google Scholar
  10. 10.
    Tombre, K., Lamiroy, B.: Pattern recognition methods for querying and browsing technical documentation. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 504–518. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Tsai, W.-H., Yu, S.-S.: Attributed string matching with merging for shape recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 453–462 (1985)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anjan Dutta
    • 1
  • Josep Lladós
    • 1
  • Umapada Pal
    • 2
  1. 1.Computer Vision CentreEdifici O, Campus UABBarcelonaSpain
  2. 2.CVPR UnitIndian Statistical InstituteKolkataIndia

Personalised recommendations