Abstract
In this paper, we propose a new approach for symbol recognition using structural signatures and a Galois Lattice as classifier. The structural signatures are based on topological graphs computed from segments which are extracted from the symbol images by using an adapted Hough transform. These structural signatures, which can be seen as dynamic paths which carry high level information, are robust towards various transformations. They are classified by using a Galois Lattice as a classifier. The performances of the proposed approach are evaluated on the GREC03 symbol database and the experimental results we obtain are encouraging.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lladós, J., Dosch, P.: Vectorial Signatures for Symbol Discrimination. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 154–165. Springer, Heidelberg (2004)
Geibel, P., Wysotzki, F.: Learning relationnal concepts with decision trees. ECAI 1996. In: 12th European Conference on Artificial Intelligence (1996)
Rusiñol, M., Lladós, J.: Symbol Spotting in Technical Drawings Using Vectorial Signatures. In: Wenyin, L., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 35–46. Springer, Heidelberg (2006)
Tombre, K., Lamiroy, B.: Graphics Recognition - from Re-engineering to Retrieval. In: Proceedings of ICDAR (2003)
Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999)
Guillas, S., Bertet, K., Ogier, J.M.: A Generic Description of the Concept Lattices’Classifier: Application to Symbol Recognition. In: Graphics Recognition: Ten Years Review and Future Perspectives - Selected papers from GREC 2005 (2006)
Geibel, P., Wysotzki, F.: Learning Relational Concepts with Decision Trees. In: Saitta, L. (ed.) Machine Learning: Proceedings of the Thirteenth International Conference, pp. 166–174. Morgan Kaufmann Publishers, San Francisco (1996)
Hough, P.: Machine Analysis of Bubble Chamber Pictures. In: International Conference on High Energy Accelerators and Instrumentation, pp. 554–556 (1959)
Wenyin, L., Dori, D.: From Raster to Vectors: Extracting Visual Information from Line Drawings. Pattern Analysis and Applications (PAA) 2(2), 10–21 (1999)
Leavers, V.F.: Survey: which hough transform. Computer Vision and Image Understanding (CVIU) 58(2), 250–264 (1993)
GREC03 Database (Graphics RECognition), www.cvc.uab.es/grec2003/symreccontest/index.htm
Etemadi, A., Schmidt, J.P., Matas, G., Illingworth, J., Kittler, J.: Low-Level Grouping of Straight Line Segments. In: British Machine Vision Conference (1991)
Iqbal, Q., Aggarwal, J.: Retrieval by Classification of Images Containing Large Manmade Objects Using Perceptual Grouping. Pattern recognition 35, 1463–1479 (2002)
Guillas, S., Bertet, K., Ogier, J.M.: Reconnaissance de Symboles Bruités à l’Aide d’un Treillis de Galois. Colloque International Francophone sur l’Ecrit et le Document, 85–90 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Coustaty, M., Guillas, S., Visani, M., Bertet, K., Ogier, JM. (2008). On the Joint Use of a Structural Signature and a Galois Lattice Classifier for Symbol Recognition. In: Liu, W., Lladós, J., Ogier, JM. (eds) Graphics Recognition. Recent Advances and New Opportunities. GREC 2007. Lecture Notes in Computer Science, vol 5046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88188-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-88188-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88184-1
Online ISBN: 978-3-540-88188-9
eBook Packages: Computer ScienceComputer Science (R0)