Abstract
Current symbol spotting and retrieval methods are not yet able to achieve the goal of both high accuracy and efficiency on large databases of line drawings. This paper presents an approach for focused symbol retrieval as step towards achieving such a goal by using concepts from image retrieval. During the off-line learning phase of the proposed approach, regions of interest are extracted from the drawings based on feature grouping. The regions are then described using an off-the-shelf descriptor. The similar descriptors are clustered, and finally a visual symbol vocabulary is learned by an SVM classifier. The vocabulary is constructed assuming no knowledge of the contents of the drawings. During on-line retrieval, the classifier recognizes the descriptors of query regions. A query can be a partial or a complete symbol, can contain contextual noise around a symbol or more than one symbol. Experimental results are presented for a database of architectural floor plans.
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
Similar content being viewed by others
References
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision (ECCV), pp. 1–22 (2004)
Delalandre, M., Pridmore, T.P., Valveny, E., Locteau, H., Trupin, É.: Building synthetic graphical documents for performance evaluation. In: Liu, W., Lladós, J., Ogier, J.-M. (eds.) GREC 2007. LNCS, vol. 5046, pp. 288–298. Springer, Heidelberg (2008)
Delalandre, M., Valveny, E., Pridmore, T., Karatzas, D.: Generation of synthetic documents for performance evaluation of symbol recognition and spotting systems. Int. J. Doc. Anal. Recogn. (IJDAR) 13(3), 187–207 (2010)
Gersho, A., Gray, R.M.: Vector Quantization Signal Compression. Kluwer, Norwell (1991)
Honkela, T., Kohonen, T.: Kohonen network. Scholarpedia 2(1), 1568 (2007)
Jacobs, D.W.: Robust and efficient detection of salient convex groups. Pattern Anal. Mach. Intell. (PAMI) 18(1), 23–37 (1996)
Jacobs, D.W.: Grouping for recognition. Technical report, Cambridge, MA, USA (1989)
Jurie, F., Triggs, B.: Creating efficient codebooks for visual recognition. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV’05), pp. 604–610 (2005)
Kong, X., Valveny, E., Snchez, G., Wenyin, L.: Symbol spotting using deformed blurred shape modeling with component indexing and voting scheme. In: Graphics Recognition Workshop (GREC), online proceedings (2011)
Linde, Y., Buzo, A., Gray, R.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)
Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theor. 28, 129–137 (1982)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, pp. 1150–1157 (1999)
Luqman, M.M., Brouard, T., Ramel, J., Llodos, J.: A content spotting system for line drawing graphic document images. In: International Conference on Pattern Recognition (ICPR), pp. 3420–3423 (2010)
Nayef, N., Breuel, T.M.: Building a symbol library from technical drawings by identifying repeating patterns. In: Kwon, Y.-B., Ogier, J.-M. (eds.) GREC 2011. LNCS, vol. 7423, pp. 69–78. Springer, Heidelberg (2013)
Nayef, N., Breuel, T.M.: Statistical grouping for segmenting symbols parts from line drawings, with application to symbol spotting. In: Proceedings of the 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 364–368 (2011)
Nayef, N., Breuel, T.M.: Efficient symbol retrieval by building a symbol index from a collection of line drawings. In: Document Recognition and Retrieval XX (DRR), vol. 8658. SPIE (2013)
Nguyen, T., Tabbone, S., Boucher, A.: A symbol spotting approach based on the vector model and a visual vocabulary. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 708–712 (2009)
Sivic, J., Zisserman, A.: Video google: efficient visual search of videos. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 127–144. Springer, Heidelberg (2006)
Valveny, E., Delalandre, M., Raveaux, R., Lamiroy, B.: Report on the symbol recognition and spotting contest. In: Kwon, Y.-B., Ogier, J.-M. (eds.) GREC 2011. LNCS, vol. 7423, pp. 198–207. Springer, Heidelberg (2013)
Yang, S.: Symbol recognition via statistical integration of pixel-level constraint histograms: a new descriptor. Pattern Anal. Mach. Intell. (PAMI) 27(2), 278–281 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nayef, N., Byeon, W., Breuel, T.M. (2014). Towards Searchable Line Drawings, a Content-Based Symbol Retrieval Approach with Variable Query Complexity. In: Lamiroy, B., Ogier, JM. (eds) Graphics Recognition. Current Trends and Challenges. GREC 2013. Lecture Notes in Computer Science(), vol 8746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44854-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-44854-0_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44853-3
Online ISBN: 978-3-662-44854-0
eBook Packages: Computer ScienceComputer Science (R0)