We take the benefit of improvements in optical devices for multimedia data storage, jointly with development of usages of AI, to apply AI techniques and ideas to very flexible retrieval among an image database. We study a progressive retrieval based on "relevance feedback", processed through a man-machine dialogue where images play an important role. In the knowledge based system we propose, EXPRIM, among a first set of images displayed to him (following a textual request or browsing in the image base), the user chooses fitting images. The system then attemps to formulate a better request, by trying to understand the user's need through his choices : the chosen images are positive illustrations of it while the rejected ones are negative illustrations. This may be viewed as a machine learning process by examples and negative examples, the concept to learn being the user's need. In the machine learning based prototype we have written in SMALLTALK on a SUN coupled with a videodisk-reader, we have tried to compare, adapt and mix some existing learning techniques. The prototype in hand is being experimented on a pilot application and progressively enhanced by adding and changing various heuristics of the knowledge base. We assume that beyond pure image retrieval, this kind of progressive requesting system may be very well suited to other applications : especially image based Computer Aided Education and Diagnosis by image.