|
Abstract : |
Image Database Management Systems (IDBMS) aim to store large collections of images, and to support efficient content-based retrieval. In this paper we explore the idea of image data modeling as a tool for describing image domains, with the twofold objectives of guiding feature extraction and incorporating semantics into the extracted summaries of images. We discuss the implementation of these ideas in the PIQ image DBMS, and demonstrate that significat gains in the expressiveness of content-based queries can be achieved. Image Database Management Systems (IDBMS) aim to store large collections of images, and to support efficient content-based retrieval of the images. Like any other DBMS, an IDBMS has a storage facility, the data is arranged according to some data model, and it has a query facility. The main IDBMS issue this paper explores is the data model and its impact on the expressiveness of the query language. The kind of queries we focus on are selection queries. In other words, we want to select a few images from the database, based on their content. Imagine a query facility that loads all the images from the database and checks each image to see if it fits the selection criterion. In all but extreme cases this query facility would be inefficient. The only other option is to have more information, |
