The process of classifying objects is a fundamental feature of most human pursuits, and the idea that people classify together those things that they find similar is both intuitive and popular across a wide range of disciplines. Therefore, similarity is important for people to make sense of the objects, structures and actions that exist in reality. Furthermore the ability to recognise a similar situation means that experience can be reused to solve problems, alleviating complex situations, save time and allow valuable resources to be used elsewhere.
Various philosophical and psychological theories of similarity have been implemented in information science. Specific information science terms associated with similarity include indexing, sub-setting, retrieval, matching, ranking, solution space, clustering, trees, categorising, equal and equivalence. Information science research in the field of similarity could be grouped under the headings of comparison, retrieval, evaluation and analysis functions. Various researchers from different information science disciplines are studying similarity. The results and ideas between some of these disciplines are interchangeable because of the overlapping interests. The different disciplines include computer vision, graphic design, pattern recognition, image analysis, databases, AI, remote sensing and GI systems.
Spatial similarity can be seen as a subset of similarity and all the entities being compared to each other have spatial components. Research areas that utilise spatial similarity are listed below in Table 1. It is acknowledged that some of the research overlaps, however it was decided to catergorise the general areas of spatial similarity research.
