%0 Journal Article
%T Implementation of neural network in CBIR systems with relevance feedback
%A Koji£¿ Nenad S.
%A £¿abarkapa Slobodan K.
%A Zaji£¿ Goran J.
%A Reljin Branimir D.
%J Journal of Automatic Control
%D 2006
%I Faculty of Electronics, Belgrade
%R 10.2298/jac0601041k
%X A content-based image retrieval system where an active learning strategy is used to gain relevance feedback (RF) is described. In this way retrieving process may be highly accelerated without significant degradation of accuracy Searching procedure was performed through the two basic steps: an objective one, based on the Euclidean distances and a subjective one based on the user's relevance feedback. Images recognized from user as the best matched to a query are labeled and used for updating the query feature vector through a RBF (radial basis function) neural network. In this process user change feature vector which became more refined and appropriate for future search. In practice, several iterative steps are sufficient, as confirmed by intensive simulations.
%K Content-based image retrieval
%K low level image descriptors
%K neural network decision
%K relevance feedback
%K subjective perception of images
%U http://www.doiserbia.nb.rs/img/doi/1450-9903/2006/1450-99030601041K.pdf