A robust framework for content-based retrieval by spatial similarity in image databases

Abstract

A framework for retrieving images by spatial similarity (FRISS) in image databases is presented. In this framework, a robust retrieval by spatial similarity (RSS) algorithm is defined as one that incorporates both directional and topological spatial constraints, retrieves similar images, and recognizes images even after they undergo translation, scaling, rotation (both perfect and multiple), or any arbitrary combination of transformations. The FRISS framework is discussed and used as a base for comparing various existing RSS algorithms. Analysis shows that none of them satisfies all the FRISS specifications. An algorithm, SIMDTC, is then presented. SIMDTC introduces the concept of a rotation correction angle (RCA) to align objects in one image spatially closer to matching objects in another image for more accurate similarity assessment. Similarity between two images is a function of the number of common objects between them and the closeness of directional and topological spatial relationships between object pairs in both images. The SIMDTC retrieval is invariant under translation, scaling, and perfect rotation, and the algorithm is able to rank multiple rotation variants. The algorithm was tested using synthetic images and the TESSA image database. Analysis shows the robustness of the SIMDTC algorithm over current algorithms.