Second-Order Belief Propagation and Its Application to Object Localization

Belief propagation (BP) has been successfully used to solve many computer vision problems, such as stereo matching, object detection and low-level vision. Although the BP algorithm provides an efficient computation framework for general graph inference, the capability of BP is limited by the fact that it only considers the first-order constraints which can simply model the distance relation between two nodes. But in many computer vision problems, this limitation will bring on serious results when the differential or the angular constraints are inherent in the problems. To resolve this limitation, we generalize the BP algorithm to consider the second-order constraints, and integrate it into the particle filtering framework to speed up the computation. In addition, we apply the proposed method to develop a face localization algorithm to demonstrate its effectiveness.