Variation in classification accuracy with number of glimpses

We consider an attention-based model that recognizes objects via a sequence of glimpses, and analyze the variation in classification accuracy with the number of glimpses. The problem of object recognition is formulated as a partially observable Markov decision process where the environment is partially observable and glimpses are actions. We show that voting from random attentional policies provides good classification accuracy if the objects in the images are aligned and of similar size. We also show that accuracy does not improve after a certain number of glimpses and sometimes decreases with more glimpses if multiple categories have similar structure. Finally, there are in general several sub-optimal policies for an object to be classified correctly, hence computing the optimal policy by solving an intractable problem is avoidable