Key points are not available for this paper at this time.
Describes a cognitive human interface for visual interaction with image database systems. The authors' approach gives a general framework of visual interaction. They adopt both an image model and a user model to interpret and operate the contents of image data from the user's viewpoint. The image model describes the physical constraints of image data, while the user model reflects the visual perception processes of the user. They propose the algorithms for typical visual interaction styles; a query by visual example (QVE) and a query by subjective descriptions (QBD). The former includes a sketch retrieval function, and a similarity retrieval function and the latter includes a sense retrieval function. These algorithms are developed for their experimental database system, the TRADEMARK and the ART MUSEUM. These functions use a pictorial index created by image analysis and a personal index automatically learned as the user model.>
Kato et al. (Tue,) studied this question.