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<p>A technique is developed based on the use of a neural network model for performing information retrieval in a pictorial information system. The neural network provides autoassociative memory operation and allows the retrieval of stored symbolic images using erroneous or incomplete information as input. The network used is based on an adaptation of the random neural network model featuring positive and negative nodes and symmetrical behavior of positive and negative signals. The network architecture considered has hierarchical structure and allows two-level operation during learning and recall. An experimental software prototype, including an efficient graphical interface, has been implemented and tested. The performance of the system has been investigated through experiments under several schemes concerning storage and reconstruction of patterns. These schemes are either based on properties of the random network or constitute adaptations of known neural network techniques.</p>
pictorial information retrieval; random neural network; autoassociative memory operation; stored symbolic images; hierarchical structure; learning; recall; software prototype; graphical interface; performance; random network; computerised picture processing; content-addressable storage; graphical user interfaces; information retrieval; neural nets; software prototyping

A. Stafylopatis and A. Likas, "A Pictorial Information Retrieval Using the Random Neural Network," in IEEE Transactions on Software Engineering, vol. 18, no. , pp. 590-600, 1992.
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