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Content-based image retrieval based on relevance feedback and reinforcement learning for medical images

Lakdashti, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.4218/etrij.11.0110.0203
  3. Abstract:
  4. 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
  5. Keywords:
  6. Image retrieval ; Reinforcement learning ; Relevance feedback ; Content based image retrieval ; Feature space ; Hyper-cubes ; Hypercube ; Medical images ; Mutation operators ; Reinforcement learning method ; Scoring methods ; Semantic content ; Feedback ; Mathematical operators ; Semantics ; Medical imaging
  7. Source: ETRI Journal ; Volume 33, Issue 2 , Apr , 2011 , Pages 240-250 ; 12256463 (ISSN)
  8. URL: http://etrij.etri.re.kr/etrij/journal/article/article.do?volume=33&issue=2&page=240