Selecting Vision Operators and Fixing Their Optimal Parameters Values Using Reinforcement Learning

  • Issam Qaffou
  • Mohamed Sadgal
  • Aziz Elfazziki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


Selecting the appropriate operators with the optimal values for their parameters represents a big challenge for users. In this paper we present a solution for this problem. This solution uses a multi-agent architecture based on reinforcement learning to automate the process of operator selection and parameter adjustment. The architecture consists of three types of agents: the User Agent, the Operator Agent and the Parameter Agent. The User Agent determines the phases of treatment, and for each phase it determines a library of possible operators and possible values of their parameters. The Operator Agent constructs all possible combinations of operators and decides for the best one. The Parameter Agent, the core of the architecture, adjusts the parameters of each combination of operators by processing a large number of images. Towards the end, the agents must offer the best combination of operators and the best values of their parameters.


Computer Vision Reinforcement Learning Multi-Agent System Parameter Adjustment Operator Selection Q-learning Segmentation 


  1. 1.
    Clouard, R., Elmoataz, A., Angot, F.: PANDORE: une bibliothèque et un environnement de programmation d’opérateurs de traitement d’images. Rapport interne du GREYC, Caen, France, Mars (1997)Google Scholar
  2. 2.
    Draper, B.A., Bins, J., Baek, K.: ADORE: Adaptive Object Recognition. Videre 1(4), 86–99 (2000)Google Scholar
  3. 3.
    Nickolay, B., Schneider, B., Jacob, S.: Parameter Optimization of an Image Processing System using Evolutionary Algorithms. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, pp. 637–644. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Taylor, G.W.: A Reinforcement Learning Framework for Parameter Control in Computer Vision Applications. In: Proceedings of the First Canadian Conference on Computer and Robot Vision (CRV 2004). IEEE (2004)Google Scholar
  5. 5.
    Sahba, F., Tizhoosh, H.R., Salama, M.: Application of reinforcement learning for segmentation of transrectal ultrasound images. BMC Medical Imaging 8, 8 (2008)CrossRefGoogle Scholar
  6. 6.
    Qaffou, I., Sadgal, M., Elfazziki, A.: A Reinforcement Learning Method to adjust Parameters of Vision Operators. In: Sixth International Conference on Intelligent Systems: Theory and Application, Rabat, pp. 23–29 (2010)Google Scholar
  7. 7.
    Qaffou, I., Sadgal, M., Elfazziki, A.: A reinforcement learning method to adjust parameter of a texture segmentation. In: The 7th International Conference on Informatics and Systems (INFOS), Cairo, pp. 1–5 (2010)Google Scholar
  8. 8.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)Google Scholar
  9. 9.
    Watkins, C.J.C.H., Dayan, P.: Q-Learning. Machine Learning 8, 279–292 (1992)zbMATHGoogle Scholar
  10. 10.
    Haroun, R.: Segmentation des tissus cérébraux sur des images par résonance magnétique. Master’s thesis, Université des sciences et de la technologie Houari Boumediène (2005)Google Scholar
  11. 11.
    Chabrier, S., Laurent, H., Rosenberger, C., Zhang, Y.J.: Supervised evaluation of synthetic and real contour segmentation results. In: European Signal Processing Conference, EUSIPCO (2006)Google Scholar
  12. 12.
    Do, M.C.: Évaluation de la segmentation d’images. Rapport final TIPE. Institut de la francophonie pour l’informatique. NanoGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Issam Qaffou
    • 1
  • Mohamed Sadgal
    • 1
  • Aziz Elfazziki
    • 1
  1. 1.Département Informatique, FSSMUniversité Cadi AyyadMorocco

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