In this thesis, we have proposed a new spatial knowledge structure, called 9D-SPA+, to represent symbolic images. Based on the 9D-SPA+ knowledge structure, we can capture the precise and compact spatial relations between objects and preserve the metric information of objects without ambiguity. Moreover, we propose an image reconstruction algorithm for the 9D-SPA+, which converts a 9D-SPA+ representation into a symbolic image for visualization and browsing. In order to obtain the orders and distances among objects in an image, we decode the 9D-SPA+ representation first and construct spatial relation graphs for reconstruction. Based on the graphs, we can easily reconstruct a symbolic image for a 9D-SPA+ representation. Finally, we present a flexible similarity retrieval algorithm for retrieving the similar images from the image database, which infers spatial relations among objects and metric information of objects to assess the difference between the query and database images. By adjusting the weights of different measures, our proposed method can meet users’ requirements. The experiment results show our proposed approach is scalable and efficient. Furthermore, by providing various measures for similarity retrieval, the experiments demonstrate our proposed similarity retrieval algorithm has discrimination power about different criteria.