Learning of personal visual impression for image database systems

Visual impression may di er with each person. User-friendly interfaces for image database systems require special retrieval methods which can adapt to the visual impression of each user. This paper describes algorithms for learning personal visual impression on visual objects. The algorithms are based on multivariate data analysis methods. These algorithms provide a model on visual perception process of each user from a small set of training examples. This model is referred to as a personal index to retrieve desired images for the user. These algorithms were implemented and examined in our graphical symbol database systems called TRADEMARK and our full color image database called ART MUSEUM.