An iterative relevance feedback learning algorithm for image retrieval systems

A new feature adaptation mechanism is proposed in this paper that captures the relevance feedback information provided by the expert users. This relevance information is retained for future usage and subsequently made available to other users. The search and retrieval processes are implemented through a two-layer connectionist network structure and the relevance feedback learning is incorporated by appropriately modifying the network structure. The developed algorithm is tested on an electro-optical imagery database collected from different underwater mine-like and non-mine-like objects.