ETRI Journal

  • 제33권2호
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  • Pages.240-250
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  • 2011
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Content-Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images

  • Lakdashti, Abolfazl (Department of Computer Engineering, University College of Rouzbahan) ;
  • Ajorloo, Hossein (Department of Computer Engineering, Sharif University of Technology)
  • 투고 : 2010.04.07
  • 심사 : 2010.10.25
  • 발행 : 2011.04.30
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초록

To enable a relevance feedback paradigm to evolve itself by users' feedback, a reinforcement learning method is proposed. The feature space of the medical images is partitioned into positive and negative hypercubes by the system. Each hypercube constitutes an individual in a genetic algorithm infrastructure. The rules take recombination and mutation operators to make new rules for better exploring the feature space. The effectiveness of the rules is checked by a scoring method by which the ineffective rules will be omitted gradually and the effective ones survive. Our experiments on a set of 10,004 images from the IRMA database show that the proposed approach can better describe the semantic content of images for image retrieval with respect to other existing approaches in the literature.

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참고문헌

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