Advertisement

An Object Recognition Model Based on Visual Grammars and Bayesian Networks

  • Elias Ruiz
  • Luis Enrique Sucar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)

Abstract

A novel proposal for a general model for object recognition is presented. The proposed method is based on symbol-relational grammars and Bayesian networks. An object is modeled as a hierarchy of features and spatial relationships using a symbol-relational grammar. This grammar is learned automatically from examples, incorporating a simple segmentation algorithm in order to generate the lexicon. The grammar is created with the elements of the lexicon as terminal elements. This representation is automatically transformed into a Bayesian network structure which parameters are learned from examples. Thus, recognition is based on probabilistic inference in the Bayesian network representation. Preliminary results in modeling natural objects are presented. The main contribution of this work is a general methodology for building object recognition systems which combines the expressivity of a grammar with the robustness of probabilistic inference.

Keywords

Visual Grammars Bayesian Networks Object Recognition 

References

  1. 1.
    Chang, L., Jin, Y., Zhang, W., Borenstein, E.: Context, Computation, and Optimal ROC Performance in Hierarchical Models. International Journal of Computer Vision 93(2), 117–140 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Felzenszwalb, P.F.: Object Detection Grammars. In: ICCV Workshops, p. 691. IEEE, Barcelona (2011)Google Scholar
  3. 3.
    Ferrucci, F., Pacini, G., Satta, G., Sessa, M.I., Tortora, G., Tucci, M., Vitiello, G.: Symbol-relation grammars: a formalism for graphical languages. Inf. Comput. 131(1), 1–46 (1996)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Gabor, D.: Theory of Communication. JIEE 93(3), 429–459 (1946)Google Scholar
  5. 5.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset. Technical Report 7694, California Institute of Technology (2007)Google Scholar
  6. 6.
    Meléndez, A., Sucar, L.E., Morales, E.F.: A Visual Grammar for Face Detection. In: Kuri-Morales, A., Simari, G.R. (eds.) IBERAMIA 2010. LNCS, vol. 6433, pp. 493–502. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Ommer, B., Buhmann, J.M.: Learning Compositional Categorization Models. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 316–329. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  9. 9.
    Zhu, S.C., Mumford, D.: A Stochastic Grammar of Images. Foundations and Trends in Computer Graphics and Vision 2(4), 259–362 (2006)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Elias Ruiz
    • 1
  • Luis Enrique Sucar
    • 1
  1. 1.Departamento de Ciencias ComputacionalesInstituto Nacional de Astrofísica, Óptica y ElectrónicaTonantzintlaMéxico

Personalised recommendations