Probabilistic coarse-to-fine object recognition
A coarse-to-fine probabilistic model for detecting objects in images is presented. Objects are composed of constellations of features, and features from a same object share the common reference frame of the image in which they are detected. Features appearance and pose are modeled by probabilistic distributions, the parameters of which are shared across features in order to allow training from few examples. In order to avoid an expensive combinatorial search, we propose a coarseto-fine strategy, inspired by the work by Lowe [30, 32] and by Geman and collaborators [1, 19]. Well-established, simple and inexpensive steps are used as successive refinement blocks that discard incorrect matching hypotheses in a cascade. The candidate hypotheses output by our algorithm are evaluated by a generative probabilistic model that takes into account each stage of the matching process. We apply our ideas to the problem of individual object recognition and test our method on several data-sets. We compare with Lowe’s algorithm and demonstrate significantly better performance.