Content-Based Spatial Information Retrieval

The search for similar configurations is an important research topic for content-based image retrieval in GIS and spatial databases. Due to the complexity of the problem, finding the fittest solution in a large database is computationally intractable. This paper incorporates the domain knowledge of spatial configuration information retrieval with two classic algorithms (Simulated Annealing Algorithm and Genetic Algorithm), designs and implements the applications of these algorithms in the domain of spatial configuration retrieval, and finally compares and evaluates the performance of the algorithms with several data sets of different sizes. And the paper utilizes a similarity framework that addresses topological, directional, and distance relations. In this framework the problem of retrieving similar spatial configurations is defined as a binary constraint satisfaction problem, and can be dealt with by the above algorithms in the application of spatial, multimedia and video databases.In geo-space the relations between spatial objects is commonly as important as themselves. And some relations may be more important according to the different application domains. In general three major types of spatial relations are usually distinguished: topological relations establish the concept of connectivity and are invariant under continuous transformations of rotation, translation, and scaling; directional relations are based on the existence of a vector space and are subject to change under rotation, while they are invariant under translation and scaling; and distance relations express spatial properties that reflect the concept of a metric and therefore change under scaling, but are invariant under translation and rotation. Among these spatial relations, topological relations have been pointed out as particularly important for describing spatial scenes, since they capture the essence of a spatial configuration - topology matters, metric refines.It's a common strategy in spatial access methods to store object approximations and use these approximations to index the data space in order to efficiently retrieve the potential objects that satisfy the result of a query. Depending on the application domain there are several options in choosing...