Spatial-Scene Similarity Queries

Assessing spatial scenes for similarity is difficult from a cognitive and computational perspective. Solutions to spatial-scene similarity assessments are sensible only if corresponding elements in the compared scenes are identified correctly. This matching process becomes increasingly complex and error-prone for large spatial scenes as it is questionable how to choose one set of associations over another or how to account quantitatively for unmatched elements. We develop a comprehensive methodology for similarity queries over spatial scenes that incorporates cognitively motivated approaches about scene comparisons, together with explicit domain knowledge about spatial objects and their relations for the relaxation of spatial query constraints. Along with a sound graph-theoretical methodology, this approach provides the foundation for plausible reasoning about spatial-scene similarity queries.