A relevance feedback mechanism for content-based image retrieval

Content-based image retrieval systems require the development of relevance feedback mechanisms that allow the user to progressively refine the system' s response to a query. In this paper a new relevance feedback mechanism is described which evaluates the feature distributions of the images judged re levant, or not relevant, by the user and dynamically updates both the simil arity measure and the query in order to accurately represent the user's par ticular information needs. Experimental results demonstrate the effectivene ss of this mechanism. (C) 1999 Elsevier Science Ltd. All rights reserved.