Rotation, Rescaling and Occlusion Invariant Object Retrieval

This paper presents a new approach for rotation, rescaling and occlusion invariant retrieval of the objects of a given database D. The objects are represented by means of many 2D views and each of them is occluded by several half-planes. The remaining visible parts (linear cuts) as well as the whole views are stored in a new database D’ and described by low-level features. Given a portion R of an image, the retrieval of the most similar object is done by generating some linear cuts of R, and by comparing their descriptors with those of the elements of D’. Some heuristic rules regarding visual similarity and geometric properties of the objects in the database drive this process. In the case R is recognized as an object partially occluded, a strategy for the reconstruction of the whole shape of R is also presented. The tests carried out on synthetic and real-world datasets showed good performances both in recognition and in reconstruction accuracy.