An Extension of the Radial Line Model to Predict Spatial Relations

Logan Servant, Camille Kurtz, Laurent Wendling

Abstract: Analysing the spatial organization of objects in images is fundamental to increasing both the understanding of a scene and the explicability of perceived similarity between images. In this article, we propose to describe the spatial positioning of objects by an extension of the original Radial Line Model to any pair of objects present in an image, by defining a reference point from the convex hulls and not the enclosing rectangles, as done in the initial version of this descriptor. The recognition of spatial configurations is then considered as a classification task where the achieved descriptors can be embedded in a neural learning mechanism to predict from object pairs their directional spatial relationships. An experimental study, carried out on different image datasets, highlights the interest of this approach and also shows that such a representation makes it possible to automatically correct or denoise datasets whose construction has been rendered ambiguous by the human evaluat

External IDs:dblp:conf/visapp/ServantKW23