Object Localization by Propagating Connectivity via Superfeatures

In this paper, we propose a part-based approach to localize objects in cluttered images. We represent object parts as boundary segments and image patches. A semi-local grouping of parts named superfeatures encodes appearance and connectivity within a neighborhood. To match parts, we integrate inter-feature similarities and intra-feature connectivity via a relaxation labeling framework. Additionally, we use a global elliptical shape prior to match the shape of the solution space to that of the object. To this end, we demonstrate the efficacy of the method for detecting various objects in cluttered images by comparing them to simple object models.