A methodology of highly interactive intra-object relevance feedback is used to retrieve images from a database. The query is adjusted using the information fed-back by the user about the relevance of previously extracted part(s) from the object itself, such that the adjusted query is a better approximation to the user's perception. Since a single query image is utiliz ed in the system according to the invention, high-performance learning techniques can be employed for this intra-object learning of user's perception. The refined query is subsequently used for intra-objec t relevance feedback where images are retrieved from the database based on parameters learnt by intra-image feedback mechanism, and the user provides feedback by ranking the retrieved objects i n order of their relevance to him or her. In the system according to the invention, intra- object learning of user's perception is expedited by utilizing the learnt parameters in the intra-obje ct relevance feedback. Furthermore, the methodology of the invention allows for building refined queries based on part(s) of the query object rather than the entire object itself, thereby reducing t he number of irrelevant objects, retrieved from the database.
AN INTERACTIVE FRAMEWORK FOR UNDERSTANDING USER'S PERCEPTIONOF MULTIMEDIA DATA