Abstract
Graphs are popular data structures used to model pair wise relations between elements from a given collection. In image processing, adjacency graphs are often used to represent the relations between segmented regions. The comparison of such graphs has been largely studied but graph matching strategies are essential to find, efficiently, similar patterns. In this paper, we propose a method to detect the recurring characters in comics books. We would like to draw attention of the reader. In this paper, the term “character” means the protagonists of the story. In our approach, each panel is represented with an attributed adjacency graph. Then, an inexact graph matching strategy is applied to find recurring structures among this set of graphs. The main idea is that the same character will be represented by similar subgraphs in the different panels where it appears. The two-step matching process consists in a node matching step and an edge validation step. Experiments show that our approach is able to detect recurring structures in the graph and consequently the recurrent characters in a comics book. The originality of our approach is that no prior object model is required the characters. The algorithm detects, automatically, all recurring structures corresponding to the main characters of the story.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Commission Internationale de l’Eclairage, Colorimetry, CIE 15.2 (1986)
Arai, K., Tolle, H.: Automatic e-comic content adaptation. Int. J. Ubiquitous Comput. (IJUC) 1(1), 1–11 (2010)
Bunke, H., Riesen, K.: Recent advances in graph-based pattern recognition with applications in document analysis. Pattern Recogn. 44(5), 1057–1067 (2011)
Bunke, H.: Error-tolerant graph matching: a formal framework and algorithms. In: Amin, A., Pudil, P., Dori, D. (eds.) SPR 1998 and SSPR 1998. LNCS, vol. 1451, pp. 1–14. Springer, Heidelberg (1998)
Cyb: Cosmozone, vol. 1. Studio Cyborga (2009)
Fischer, A., Suen, C.Y., Frinken, V., Riesen, K., Bunke, H.: A fast matching algorithm for graph-based handwriting recognition. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds.) GbRPR 2013. LNCS, vol. 7877, pp. 194–203. Springer, Heidelberg (2013)
Chan, C.H., Leung, H., Komura, T.: Automatic panel extraction of color comic images. In: Ip, H.H.-S., Au, O.C., Leung, H., Sun, M.-T., Ma, W.-Y., Hu, S.-M. (eds.) PCM 2007. LNCS, vol. 4810, pp. 775–784. Springer, Heidelberg (2007)
Ho, A.K.N., Burie, J.C., Ogier, J.M.: Panel and speech balloon extraction from comic books. In: DAS 2012, Tenth IAPR International Workshop on Document Analysis Systems, Gold Coast, Australia (2012)
Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theor. 8(2), 179–187 (1962)
Le blog bd de cyb. http://www.cosmozone.fr/tag/Cosmozone%20p%C3%A9riode%201/
Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Ishii, D., Watanabe, H.: A study on frame position detection of digitized comics images. In: Proceedings of Workshop on Picture Coding and Image Processing, PCSJ2010/IMPS2010, Nagoya, Japan, December 2010, pp. 124–125 (2010)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 313–320 (2001)
Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. Data Min. Knowl. Discov. 11, 243–271 (2005)
Lopresti, D., Wilfong, G.: A fast technique for comparing graph representations with applications to performance evaluation. Int. J. Doc. Anal. Recogn. 6(4), 219–229 (2003)
Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recogn. 36(10), 2210–2230 (2003)
Luqman, M.M., Ramel, J.Y., Llads, J., Brouard, T.: Fuzzy multilevel graph embedding. Pattern Recogn. 46(2), 551–565 (2013)
Messmer, B.T., Bunke, H.: A new algorithm for error-tolerant subgraph isomorphism detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(5), 493–504 (1998)
Rigaud, C., Karatzas, D., Van de Weijer, J., Burie, J.C., Ogier, J.M.: Automatic text localisation in scanned comic books. In: Proceedings of the 8th International Conference on Computer Vision Theory and Applications (VISAPP), pp. 814–819 (2013)
Rigaud, C., Tsopze, N., Burie, J.-C., Ogier, J.-M.: Robust frame and text extraction from comic books. In: Kwon, Y.-B., Ogier, J.-M. (eds.) GREC 2011. LNCS, vol. 7423, pp. 129–138. Springer, Heidelberg (2013)
Shapiro, L., Haralick, R.: Structural descriptions and inexact matching. IEEE Trans. Pattern Anal. Mach. Intell. 3(5), 504–519 (1981)
Shervashidze, N., Vishwanathan, S.V.N., Petri, T., Mehlhorn, K., Borgwardt, K.M.: Efficient graphlet kernels for large graph comparison. In: Twelfth International Conference on Artificial Intelligence and Statistics, pp. 488–495 (2009)
Sidere, N., Héroux, P., Ramel, J.Y.: Embedding labeled graphs into occurence matrix. In: Proceedings of the IAPR Workshop on Graphics Recognition, GREC 2009, La Rochelle, France, pp. 44–50 (2009)
Sun, W., Kise, K.: Similar manga retrieval using visual vocabulary based on regions of interest. In: Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR2011), October 2011, pp. 1075–1079 (2011)
Tanaka, T., Shoji, K., Toyama, F., Miyamichi, J.: Layout analysis of tree-structured scene frames in comic images. In: Proceedings of International Joint Conference on Artificial Intelligence, IJCAI-07, Hyderabad, India, January 2007, pp. 2885–2890 (2007)
Tsai, W.H., Fu, K.S.: Error-correcting isomorphisms of attributed relational graphs for pattern analysis. IEEE Trans. Syst. Man Cybern. 9(12), 757–768 (1979)
Yan, X., Han, J.: Span: graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 721–725 (2002)
Acknowledgement
This work was supported by the European Regional Development Fund, the region Poitou-Charentes (France), the General Council of Charente Maritime (France) and the town of La Rochelle (France).
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
Ho, H.N., Rigaud, C., Burie, JC., Ogier, JM. (2014). Detecting Recurring Deformable Objects: An Approximate Graph Matching Method for Detecting Characters in Comics Books. 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_10
Download citation
DOI: https://doi.org/10.1007/978-3-662-44854-0_10
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)