Design and evaluation of spatial similarity approaches for image retrieval

Similarity retrieval by spatial image content (i.e., using multiple objects and their relationships in space) is an open problem which has received considerable attention in the literature. The most powerful approaches of spatial image content representation and retrieval are “Attributed Relational Graphs” (ARGs) and “Symbolic Projections” (e.g., 2D Strings). In this work, a framework is proposed for studying the performance of such spatial similarity approaches in Image DataBases (IDBs). The classical ARG and 2D string matching methods are evaluated. Several variants of ARG and 2D string methods for improving their accuracy and speeding-up their time responses are also proposed and tested. A critical analysis of the performance of all these methods is presented. The analysis indicates that, in retrieving images by spatial content, retrieval response time and accuracy are traded-off. Key-Words: image database, image retrieval, spatial relationships, attributed relational graphs, 2D strings.