Heuristic algorithms for similar configuration retrieval in spatial databases

Abstract. The search for similar configurations is an important research topic for content-based image retrieval in G.I.S. and spatial databases. Due to the complexity of the problem, finding the fittest solution in a large database is computationally intractable. Our work is focused on designing, implementing and experimentally evaluating two heuristic algorithms, an evolutionary and a hill-climbing one, that provide an approximate solution. With the use of spatial indexes we manage to efficiently deal with considerably large queries. We utilize a similarity framework that addresses topological, directional and distance relations. In this framework the problem of retrieving similar configurations is defined as a binary constraint satisfaction problem. Our work complements the existing work on similarity retrieval with two efficient, stochastic, algorithms.