Open AccessContent‐Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for Medical Images
Author(s) -
Lakdashti Abolfazl,
Ajorloo Hossein
Publication year - 2011
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.11.0110.0203
Subject(s) - relevance feedback , computer science , reinforcement learning , relevance (law) , hypercube , feature (linguistics) , set (abstract data type) , image retrieval , image (mathematics) , content based image retrieval , artificial intelligence , space (punctuation) , genetic algorithm , feature vector , machine learning , data mining , information retrieval , pattern recognition (psychology) , linguistics , philosophy , operating system , parallel computing , political science , law , programming language
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.