Showing papers on "Content-based image retrieval published in 1992"

Database architecture for content-based image retrieval

Abstract: This paper describes visual interaction mechanisms for image database systems. The typical mechanisms for visual interactions are query by visual example and query by subjective descriptions. The former includes a sketch retrieval function and a similarity retrieval function, while the latter includes a sense retrieval function. We 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 graphical features of image data, while the user model reflects the visual perception processes of the user. These models, automatically created by image analysis and statistical learning, are referred to as abstract indexes stored in relational tables. These algorithms are developed on our experimental database system, the TRADEMARK and the ART MUSEUM.

A sketch retrieval method for full color image database-query by visual example

Abstract: Gives a basic idea and its fundamental algorithms of the visual interface for image database systems. The QVE (Query by Visual Example) accepts a sketch roughly drawn by a user to retrieve the original image and the similar images. The system evaluates the similarity between the rough sketch, i.e. a visual example, and each of the image data in the database automatically. The QVE interface is implemented and examined on an experimental electronic art gallery called ART MUSEUM. This paper also gives some experimental results and a current evaluation. The algorithms are quite effective for content based image retrieval. >

Object-oriented approach to design a content-based image retrieval model

Abstract: The objective of this article is to offer both an image description model in three levels and an adapted image retrieval process. The proposed model allows the description of: the image globally with a classical manner (a list of concepts); the objects separately contained in the image (component objects); and relations between the component objects. This allows more semantic aspects of the image structure to be expressed. It is also possible to use fuzzy information in each level of the description. To build this model, we used the concepts of the object-oriented approach: generalization/specialization, aggregation, and inheritance. This approach is born from a strong need expressed in several fields such as medicine, CAD/CAM, architecture, and the teledetection industry, to be able to retrieve and describe images, their component objects, and the relations between them more precisely, with their own vocabulary and concepts.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.