Image Database Design Based on 9D-SPA Knowledge Representation for Spatial Relations

Abstract

Spatial relationships between objects are important features for designing a contentbased image retrieval system. In this paper, we propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as iconic indexing and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on Minimum Bounding Rectangles or centroids of objects. The similarity measures using 9D-SPA representation provides a wide range of fuzzy matching capability in similarity retrieval to meet different user’s requirements. More importantly, the 9D-SPA representation can be incorporated into a three-level index structure to help reducing the search space of each query processing. The experimental results demonstrated that, on an average, only 0.1254% ∼ 1.6829 % of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures. This is a significant improvement over pure ”iconic indexing” without using any higher-level index scheme.