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

详细信息    查看全文

• 作者：Martin Stommel ; Klaus-Dieter Kuhnert ; Weiliang Xu
• 关键词：Inexact graph matching ; Sub ; graph matching ; Cartoon/manga recognition
• 刊名：Pattern Analysis & Applications
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：19
• 期：1
• 页码：55-67
• 全文大小：2,370 KB
• 参考文献：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 420–434
  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 1–19
  3.Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. Computer Vision Pattern Recog (CVPR), p 8
  4.Auwatanamongkol S (2007) Inexact graph matching using a genetic algorithm for image recognition. Pattern Recog Lett 28:1428–1437CrossRef
  5.Bai L, Hancock E (2013) Graph kernels from the jensen-shannon divergence. J Math Imaging Vis 47(1–2):60–69CrossRef
  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 373–382
  7.Bay H, Ess A, Tuytelaars T, van Gool L (2006) SURF: speeded up robust features. Comput Vis Image Underst (CVIU) 110(3):346–359CrossRef
  8.Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is “nearest neighbor” meaningful? In: International Conference on Database Theory (ICDT), pp 217–235
  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 19–28
  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 775–784CrossRef
  11.Chung FRK (1997) Spectral graph theory, CBMS regional conference series in mathematics, vol 92. American Mathematical Society, Providence
  12.Crandall DJ, Felzenszwalb PF, Huttenlocher DP (2005) Spatial priors for part-based recognition using statistical models. Comput Vis Pattern Recog (CVPR), pp 10–17
  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, Berlin
  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): 37–50
  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. http://​www.​robots.​ox.​ac.​uk/​vgg
  16.Gärtner T (2003) A survey of kernels for structured data. SIGKDD Explor Newsl 5(1):49–58CrossRef
  17.Han F, Zhu SC (2005) Bottom-up/top-down image parsing by attribute graph grammar. Proc Tenth IEEE Int Conf Comput Vis (ICCV) 2:1778–1785
  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 690–697
  19.Lafferty J, Lebanon G (2005) Diffusion kernels on statistical manifolds. J Mach Learn Res 6:129–163MathSciNet
  20.Lowe DG (1999) Object recognition from local scale-invariant features. In: International Converence on Computer Vision (ICCV), pp 1150–1157
  21.Merhej LI, Stommel M, Müller MG (2012) Style & Tile! comics in interactive environments. In: The Third International Comics Conference: Comics Rock, June 28–29
  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)
  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):431–439
  24.Qiu H, Hancock ER (2006) Graph embedding using commute time. Struct Syntactic Stat Pattern Recog Lect Notes Comput Sci 4109:441–449
  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 316–323
  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)
  27.Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905
  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 129–138
  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): 25–36
  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 8–9
  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), IEEE
  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 1075–1079
  33.Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323CrossRef
  34.Tuzel O, Porikli F, Meer P (2006) Region covariance: a fast descriptor for detection and classification. Eur Conf Comput Vis (ECCV)
  35.Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRef
  36.Wilson RC, Hancock ER, Luo B (2005) Pattern vectors from algebraic graph theory. IEEE Trans Pattern Anal Mach Intell 27(7):1112–1124CrossRef
  
• 作者单位：Martin Stommel (1)
  Klaus-Dieter Kuhnert (2)
  Weiliang Xu (1)
  
  1. Mechatronics Group, Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand
  2. Institute of Real Time Learning Systems, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
  
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  
• 出版者：Springer London
• ISSN：1433-755X

文摘

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. Keywords Inexact graph matching Sub-graph matching Cartoon/manga recognition