Skip to main content
Log in

A Problem of Pattern Recognition in Arrays of Interrelated Objects. Recognition Algorithm

  • Adaptive and Robust Systems
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

The classical methods of pattern recognition theory presuppose the independence of elements of a recognizable set. Therefore, even in the case of interrelated objects, the decision on the class of each object is taken independent of the decision on the classes of other objects. At the same time, it is obvious that in this case it is necessary to make consistent decisions on the classes of elements of the interrelated array. In particular, this necessity stems from the prior supposition that neighboring objects of the interrelated array more often belong to one class than to different classes. In many practical problems, such a prior supposition proves to be very natural, enabling one to develop effective recognition algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  1. Vintsyuk, T.K., Analiz, raspoznavanie i interpretatsiya rechevykh signalov (Analysis, Recognition, and Interpretation of Voice Signals), Kiev: Naukova Dumka, 1987.

    Google Scholar 

  2. Rabiner, L.R., A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. IEEE, 1989, vol. 77, no.2, pp. 257–285.

    Article  Google Scholar 

  3. Mottl, V.V. and Muchnik, I.B., Skrytye markovskie modeli v strukturnom analize signalov (Hidden Markov Models in Structural Analysis of Signals), Moscow: Fizmatlit, 1999.

    Google Scholar 

  4. Kickpatrick, S., Gelatt, C., and Vecci, M., Optimization by Simulated Annealing, Sci., 1983, vol. 220, pp. 671–680.

    Google Scholar 

  5. German, S. and German, D., Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Trans. PAMI, 1984, vol. 6, pp. 721–741.

    Google Scholar 

  6. Besag, J.E., On the Statistical Analysis of Dirty Pictures (with Discussions), J. Royal Stat. Soc., 1986, B48, pp. 259–302.

    MathSciNet  Google Scholar 

  7. Dvoenko, S.D., Kopylov, A.B., and Mottl, V.V., The Problem of Pattern Recognition in Arrays of Interrelated Objects. Statement of the Problem and Basic Applications, Avtom. Telemekh., 2004, no. 1, pp. 143–158.

  8. Li, R., Optimal'nye otsenki, opredelenie kharakteristik i upravlenie (Optimal Estimates, Definition of Characteristics, and Control), Moscow: Nauka, 1966.

    Google Scholar 

  9. Sage, A.P. and Mels, J.L., Estimation Theory with Application to Communications and Control, New York: McGraw Hill, 1971.

    Google Scholar 

  10. Yarlykov, M.S. and Mironov, M.A., Markovskaya teoriya otsenivaniya sluchainykh protsessov (Markov Theory of Estimation of Random Processes), Moscow: Radio i Svyaz', 1993.

    Google Scholar 

  11. Kemeny, J. and Snell, J., Finite Markov Chains, Princeton: Van Nostrand, 1960.

    Google Scholar 

  12. Feller, W., Introduction to Probability Theory and Its Applications, London: Wiley, 1957.

    Google Scholar 

  13. Fisher, R.A., The Use of Multiple Measurements in Taxonomic Problems, Ann. Eugenics, 1936, vol. 7, pp. 179–188.

    Google Scholar 

  14. Fisher, R.A., The Use of Multiple Measurements in Taxonomic Problems, in Sovremennye problemy kibernetiki (Modern Problems of Cybernetics), Moscow: Znanie, 1979, no. 11, pp. 6–20.

    Google Scholar 

  15. Duda, R. and Hart, P., Pattern Classification and Scene Analysis, New York: Wiley, 1973. Translated under the title Raspoznavanie obrazov i analiz stsen, Moscow: Mir, 1976.

    Google Scholar 

  16. Mottl, V.V., Dvoenko, S.D., Levyant, V.B., and Muchnik, I.B., Pattern Recognition in Spatial Data: A New Method of Seismic Explorations for Oil and Gas in Crystalline Basement Rocks, Proc. 15th ICPR'2000, Barcelona, Sept. 3–8, 2000, vol. 3, pp. 210–213.

    Google Scholar 

  17. Pavlidis, T., Algoritmy mashinnoi grafiki i obrabotki izobrazhenii (Algorithms of Computer Graphics and Image Processing), Moscow: Radio i Svyaz', 1986.

    Google Scholar 

  18. Biologiya (Biology), Yarygin, V.N., Ed., Moscow: Visshaya Shkola, 2001.

    Google Scholar 

  19. Kozinets, B.N., The Recursive Algorithm for Separation of Convex Hulls of Two Sets, in Algoritmy obucheniya raspoznavaniya obrazov (Pattern Recognition Learning Algorithms), Vapnik, V.N., Ed., Moscow: Sovetskoe Radio, 1973, pp. 43–50.

    Google Scholar 

  20. Vapnik, V.N. and Chervonenkis, A.Ya., Teoriya raspoznavaniya obrazov (statisticheskie problemy obucheniya) (Pattern Recognition Theory: Statistical Problems of Learning), Moscow: Nauka, 1974.

    Google Scholar 

  21. Cortes, C. and Vapnik, V., Support-Vector Networks, Machine Learning, 1995, vol. 20, no.3, pp. 273–297.

    Google Scholar 

  22. Vapnik, V.N., Statistical Learning Theory, New York: Wiley, 1998.

    Google Scholar 

  23. Bishop, C.M. and Tipping, M.E., Variational Relevance Vector Machines, Proc. 16th Conf. On Uncertainty in Artificial Intelligence, 2000, pp. 46–53.

  24. Bishop, C.M. and Winn, J.M., Non-linear Bayesian Image Modeling, Proc. 6th ECCV' 2000, Dublin, 2000, vol. 1, pp. 3–17.

    Google Scholar 

  25. Corduneanu, A. and Bishop, C.M., Variational Bayesian Model Selection for Mixture Distributions, in Artificial Intelligence and Statistics, Jaakkola, T. and Richardson, T., Eds., 2001, pp. 27–34.

Download references

Author information

Authors and Affiliations

Authors

Additional information

__________

Translated from Avtomatika i Telemekhanika, No. 12, 2005, pp. 162–176.

Original Russian Text Copyright © 2005 by Dvoenko, Kopylov, Mottl.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dvoenko, S.D., Kopylov, A.V. & Mottl, V.V. A Problem of Pattern Recognition in Arrays of Interrelated Objects. Recognition Algorithm. Autom Remote Control 66, 2019–2032 (2005). https://doi.org/10.1007/s10513-005-0232-9

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10513-005-0232-9

Keywords

Navigation