A Probabilistic Cascade of Detectors for Individual Object Recognition
A probabilistic system for recognition of individual objects is presented. The objects to recognize 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, our recognition system is organized as a cascade of well-established, simple and inexpensive detectors. 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.
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