Object Class Recognition with Many Local Features

In this paper we present a method to recognize an object class by learning a statistical model of the class. The probabilistic model decomposes the appearance of an object class into a set of local parts and models the appearance, relative location, co-occurrence, and scale of these parts. However, in many object classification approaches that use local features, learning the parameters is exponential in the number of parts because of the problem of matching local features in the image to parts in the model. In this paper we present a learning method that overcomes this difficulty by adding new parts to the model incrementally, using the Maximum-Likelihood framework. When we add a part to the model, a set of candidate parts are selected and the part that increases the likelihood of the data the most is added to the model. Once this part is added to the model, the parameters for all parts up to this point are updated using EM. The learning and recognition in this approach are translation and scale invariant, robust to background clutter, and has less restriction on the number of parts in the model. The validity of the approach is demonstrated on a real world dataset, where the approach is competitive with others, and where the learning for a rich model is much faster than previous approaches.