Bayesian active object recognition via Gaussian process regression

This paper is concerned with a Bayesian approach of actively selecting camera parameters in order to recognize a given object from a finite set of object classes. Gaussian process regression is applied to learn the likelihood of image features given the object classes and camera parameters. In doing so, the object recognition task can be treated as Bayesian state estimation problem. For improving the recognition accuracy and speed, the selection of appropriate camera parameters is formulated as a sequential optimization problem. Mutual information is considered as optimization criterion, which aims at maximizing the information from camera observations or equivalently at minimizing the uncertainty of the state estimate.