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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)

Abstract

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.

Keywords

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

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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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