Abstract
Recognition systems for complex and deformable objects must handle a variety of possible object appearances. In this paper, a compositional approach to this problem is studied which splits the set of possible appearances into easier sub-problems. To this end, a grammar is introduced that represents objects by a hierarchy of increasingly abstract visual alphabets. These alphabets store features, complex patterns and different views of objects. The geometrical constraints are optimised to the respective level of abstraction. The performance of the method is demonstrated on a cartoon data base with high intra-class variance.
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Behmo, R., Paragios, N., Prinet, V.: Graph Commute Times for Image Representation. In: IEEE Conference in Computer Vision and Pattern Recognition, CVPR 2008 (2008)
Crandall, D.J., Felzenszwalb, P.F., Huttenlocher, D.P.: Spatial Priors for Part- Based Recognition Using Statistical Models. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10–17 (2005)
Crandall, D.J., Huttenlocher, D.P.: Composite Models of Objects and Scenes for Category Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2007)
Fergus, R., Perona, P., Zisserman, A.: 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, pp. 443–461. Springer, Heidelberg (2006)
Han, F., Zhu, S.C.: Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 1778–1785 (2005)
Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. Technical Report IRP-TR-03-15, School of Computer Science, Carnegie Mellon University (2004)
Lee, W.-J., Duin, R.: An Inexact Graph Comparison Approach in Joint Eigenspace. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) S+SSPR 2008. LNCS, vol. 5342, pp. 35–44. Springer, Heidelberg (2008)
Lin, Z., Hua, G., Davis, L.: Multiple Instance Feature for Robust Part-based Object Detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009)
Liu, J., Yang, Y., Shah, M.: Learning Semantic Visual Vocabularies Using Diffusion Distance. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009)
Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: Proc. of the International Conference on Computer Vision (ICCV), Kerkyra, Greece, September 1999, vol. 2, pp. 1150–1157 (1999)
Mikolajczyk, K., Leibe, B., Schiele, B.: Local Features for Object Class Recognition. In: International Conference on Computer Vision ICCV 2005 (October 2005)
Mikolajczyk, K., Leibe, B., Schiele, B.: Multiple Object Class Detection with a Generative Model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2006, vol. 1, pp. 26–36 (2006)
Miyashita, Y., Sakai, K., Higuchi, S.-I., Masui, N.: Localization of Primal Long-Term Memory in the Primate Temporal Cortex. In: Squire, L.R., Weinberger, N.M., Lynch, G., McGaugh, J.L. (eds.) Memory: Organization And Locus of Change (1991)
Miyashita, Y.: Inferior Temporal Cortex: Where Visual Perception Meets Memory. Annual Reviews of Neuroscience 16, 245–265 (1993)
Nielsen, K.J., Logothetis, N.K., Rainer, G.: Object features used by humans and monkeys to identify rotated shapes. Journal of Vision 8(2), 1–15 (2008)
Salzmann, M., Urtasun, R., Fua, P.: Local Deformation Models for Monocular 3D Shape Recovery. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)
Stark, M., Schiele, B.: How good are local features for classes of geometric objects. In: IEEE 11th International Conference on Computer Vision ICCV, pp. 1–8 (2007)
Stommel, M., Kuhnert, K.-D.: A Hierarchical Model for the Recognition of Deformable Objects. In: Int’l Conf. on Computer Vision and Graphics 2008 (ICCVG 2008), Warsaw, Poland, November 10–12 (2008)
Tanaka, K.: Inferotemporal cortex and object vision. Annual Reviews of Neuroscience 19, 109–139 (1996)
Weber, M., Welling, M., Perona, P.: Unsupervised Learning of Models for Recognition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 628–641. Springer, Heidelberg (2000)
Yang, L., Jin, R., Sukthankar, R., Jurie, F.: Unifying Discriminative Visual Codebook Generation with Classifier Training for Object Category Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)
Zass, R., Shashua, A.: Probabilistic Graph and Hypergraph Matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)
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Stommel, M., Kuhnert, KD. (2010). Visual Alphabets on Different Levels of Abstraction for the Recognition of Deformable Objects. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_20
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DOI: https://doi.org/10.1007/978-3-642-14980-1_20
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