Learning of personal visual impression for image database systems
Takio Kurita, Takumi Kato · 2002
Visual impression may differ with each person. User-friendly interfaces for image database systems require special retrieval methods which can adapt to the visual impression of each user. Algorithms for learning personal visual impressions of visual objects are described. 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 a graphical symbol database system called TRADEMARK and a full color image database called ART MUSEUM.>