Pattern Analysis and Applications

, Volume 19, Issue 1, pp 55–67 | Cite as

Inexact matching of structural models based on the duality of patterns and classifiers

  • Martin Stommel
  • Klaus-Dieter Kuhnert
  • Weiliang Xu
Theoretical Advances


In many computer vision problems, the essential information can be most easily interpreted in the form of structural models. However, the computation of distances between structural models can be difficult, since small changes in the underlying image data often cause significant differences in the graph layout. The other way around, changes in the link structure of a graph often mean complex changes in feature attributes or do not correspond to any valid visual data at all. In contrast, structural models can often be used conveniently for the classification of a pattern. Based on this observation, we propose to shift the graph matching problem to the easier problem of matching classifiers and patterns. The similarity between two models is detected if a model serving as a pattern can be recognised by another model serving as classifier. To extend this measure beyond single binary digits, models are compared to whole sets of other models. Single models are described by the vector of similarities to the set. Further comparisons between models can be done using appropriate distance functions on the vector representation. The use of the method is demonstrated in a graph clustering task. We also discuss the numerical stability of the method with respect to the dimensionality of the descriptors.


Inexact graph matching Sub-graph matching Cartoon/manga recognition 


  1. 1.
    Aggarwal CC, Hinneburg A, Keim DA (2001) On the surprising behavior of distance metrics in high dimensional space. In: International Conference on Database Theory (ICDT), pp 420–434Google Scholar
  2. 2.
    Ahuja N, Todorovic S (2010) From region based image representation to object discovery and recognition. Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (S+SSPR), LNCS 6218, pp 1–19Google Scholar
  3. 3.
    Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. Computer Vision Pattern Recog (CVPR), p 8Google Scholar
  4. 4.
    Auwatanamongkol S (2007) Inexact graph matching using a genetic algorithm for image recognition. Pattern Recog Lett 28:1428–1437CrossRefGoogle Scholar
  5. 5.
    Bai L, Hancock E (2013) Graph kernels from the jensen-shannon divergence. J Math Imaging Vis 47(1–2):60–69CrossRefMATHGoogle Scholar
  6. 6.
    Bai X, Hancock ER (2005) Recent results on heat Kernel embedding of graphs. Graph-based representations in pattern recognition (GbRPR), 5th IAPR International Workshop vol 3434, pp 373–382Google Scholar
  7. 7.
    Bay H, Ess A, Tuytelaars T, van Gool L (2006) SURF: speeded up robust features. Comput Vis Image Underst (CVIU) 110(3):346–359CrossRefGoogle Scholar
  8. 8.
    Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is “nearest neighbor” meaningful? In: International Conference on Database Theory (ICDT), pp 217–235Google Scholar
  9. 9.
    Burge M, Burger W, Mayr W (1996) Recognition and learning with polymorphic structural components. In: Proceedings of the 13th ICPR, Vienna, Austria 1, pp 19–28Google Scholar
  10. 10.
    Chan C, Leung H, Komura T (2007) Automatic panel extraction of color comic images. In: Ip HS, Au O, Leung H, Sun MT, Ma WY, Hu SM (eds) Advances in multimedia information processing GPCM 2007, vol 4810, Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 775–784CrossRefGoogle Scholar
  11. 11.
    Chung FRK (1997) Spectral graph theory, CBMS regional conference series in mathematics, vol 92. American Mathematical Society, ProvidenceGoogle Scholar
  12. 12.
    Crandall DJ, Felzenszwalb PF, Huttenlocher DP (2005) Spatial priors for part-based recognition using statistical models. Comput Vis Pattern Recog (CVPR), pp 10–17Google Scholar
  13. 13.
    Disney W (2000) Lustiges Taschenbuch, vol 204, 320, 323, 327, 328, 336, 357, 367, Spezial 13, Enten 7 edn, 20, Sonderband 12. Egmont Ehapa, BerlinGoogle Scholar
  14. 14.
    Dong Y, Gao S, Tao K, Liu J, Wang H (2013) Performance evaluation of early and late fusion methods for generic semantics indexing. Pattern Anal Appl 17(1): 37–50Google Scholar
  15. 15.
    Fergus R, Perona P, Zisserman A (2006) A sparse object category model for efficient learning and complete recognition. In: Ponce J, Hebert M, Schmid C, Zisserman A (eds) Toward category-level object recognition, LNCS, vol 4170. Springer, New York, pp 443–461.
  16. 16.
    Gärtner T (2003) A survey of kernels for structured data. SIGKDD Explor Newsl 5(1):49–58CrossRefGoogle Scholar
  17. 17.
    Han F, Zhu SC (2005) Bottom-up/top-down image parsing by attribute graph grammar. Proc Tenth IEEE Int Conf Comput Vis (ICCV) 2:1778–1785Google Scholar
  18. 18.
    Jain B, Obermayer K (2010) Large sample statistics in the domain of graphs. Joint IAPR International Workshop on Structural, Syntactic and Statistical, Pattern Recognition (S+SSPR), pp 690–697Google Scholar
  19. 19.
    Lafferty J, Lebanon G (2005) Diffusion kernels on statistical manifolds. J Mach Learn Res 6:129–163MathSciNetMATHGoogle Scholar
  20. 20.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: International Converence on Computer Vision (ICCV), pp 1150–1157Google Scholar
  21. 21.
    Merhej LI, Stommel M, Müller MG (2012) Style & Tile! comics in interactive environments. In: The Third International Comics Conference: Comics Rock, June 28–29Google Scholar
  22. 22.
    Mikolajczyk K, Leibe B, Schiele B (2006) Multiple object class detection with a generative model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR’06)Google Scholar
  23. 23.
    Pavani SK, Delgado-Gomez D, Frangi AF (2012) Gaussian weak classifiers based on co-occurring haar-like features for face detection. Pattern Anal Appl 17(2):431–439Google Scholar
  24. 24.
    Qiu H, Hancock ER (2006) Graph embedding using commute time. Struct Syntactic Stat Pattern Recog Lect Notes Comput Sci 4109:441–449Google Scholar
  25. 25.
    Rahman NA, Hancock E (2010) Commute time convolution kernels for graph clustering. Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (S+SSPR), LNCS 6218, pp 316–323Google Scholar
  26. 26.
    Saund E (2011) A graph lattice approach to maintaining dense collections of subgraphs as image features. 11th International Conference on Document Analysis and Recognition (ICDAR)Google Scholar
  27. 27.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905 Google Scholar
  28. 28.
    Sorensen L, Duin R, de Bruijne M, Lee WJ, Tax D, Loog M (2010) Dissimilarity-based multiple instance learning. Joint IAPR International Workshop on Structural, Syntactic and Statistical Pattern Recognition (S+SSPR), LNCS 6218, pp 129–138Google Scholar
  29. 29.
    Stommel M (2010) Binarising SIFT-descriptors to reduce the curse of dimensionality in histogram-based object recognition. Int J Sign Process Image Process Pattern Recogn (IJSIP) 3(1): 25–36Google Scholar
  30. 30.
    Stommel M, Herzog O (2010) Learning of face components in coherent and disturbed constellations. In: Image and Vision Computing New Zealand (IVCNZ), Queenstown, New Zealand, Nov 8–9Google Scholar
  31. 31.
    Stommel M, Kuhnert KD (2008) Part aggregation in a compositional model based on the evaluation of feature cooccurrence statistics. In: International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEEGoogle Scholar
  32. 32.
    Sun W, Kise K (2011) Similar manga retrieval using visual vocabulary based on regions of interest. 2013 12th International Conference on Document Analysis and Recognition 0, pp 1075–1079Google Scholar
  33. 33.
    Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323CrossRefGoogle Scholar
  34. 34.
    Tuzel O, Porikli F, Meer P (2006) Region covariance: a fast descriptor for detection and classification. Eur Conf Comput Vis (ECCV)Google Scholar
  35. 35.
    Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar
  36. 36.
    Wilson RC, Hancock ER, Luo B (2005) Pattern vectors from algebraic graph theory. IEEE Trans Pattern Anal Mach Intell 27(7):1112–1124CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Martin Stommel
    • 1
  • Klaus-Dieter Kuhnert
    • 2
  • Weiliang Xu
    • 1
  1. 1.Mechatronics Group, Department of Mechanical EngineeringUniversity of AucklandAucklandNew Zealand
  2. 2.Institute of Real Time Learning Systems, Department of Electrical Engineering and Computer ScienceUniversity of SiegenSiegenGermany

Personalised recommendations