Integrating symbolic images into a multimedia database system using classification and abstraction approaches |
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Authors: | Aya Soffer Hanan Samet |
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Affiliation: | (1) Computer Science Department and Center for Automation Research and Institute for Advanced Computer Science, University of Maryland at College Park, College Park, Maryland 20742, USA; E-mail: {aya, hjs}@umiacs.umd.edu , US |
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Abstract: | Symbolic images are composed of a finite set of symbols that have a semantic meaning. Examples of symbolic images include
maps (where the semantic meaning of the symbols is given in the legend), engineering drawings, and floor plans. Two approaches
for supporting queries on symbolic-image databases that are based on image content are studied. The classification approach
preprocesses all symbolic images and attaches a semantic classification and an associated certainty factor to each object
that it finds in the image. The abstraction approach describes each object in the symbolic image by using a vector consisting
of the values of some of its features (e.g., shape, genus, etc.). The approaches differ in the way in which responses to queries
are computed. In the classification approach, images are retrieved on the basis of whether or not they contain objects that
have the same classification as the objects in the query. On the other hand, in the abstraction approach, retrieval is on
the basis of similarity of feature vector values of these objects. Methods of integrating these two approaches into a relational
multimedia database management system so that symbolic images can be stored and retrieved based on their content are described.
Schema definitions and indices that support query specifications involving spatial as well as contextual constraints are presented.
Spatial constraints may be based on both locational information (e.g., distance) and relational information (e.g., north of).
Different strategies for image retrieval for a number of typical queries using these approaches are described. Estimated costs
are derived for these strategies. Results are reported of a comparative study of the two approaches in terms of image insertion
time, storage space, retrieval accuracy, and retrieval time.
Received June 12, 1998 / Accepted October 13, 1998 |
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Keywords: | :Symbolic-image databases – Multimedia databases – Retrieval by content – Spatial databases – Image indexing – Query optimization |
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