Mutual Boost ing for Contextual Inference
Mutual Boosting is a method aimed at incorporating contextual information to augment object detection. When multiple detectors of objects and parts are trained in parallel using AdaBoost [1], object detectors might use the remaining intermediate detectors to enrich the weak learner set. This method generalizes the efficient features suggested by Viola and Jones [2] thus enabling information inference between parts and objects in a compositional hierarchy. Eye-, nose-, mouthand face-detectors are trained using the Mutual Boosting framework. Results show that the method outperforms applications overlooking contextual information. Mutual referencing of object detectors appears in surprisingly early stages of the training process, thus indicating that contextual information has an essential contribution to object detection processes. We suggest that achieving contextual integration is a step toward human-like detection capabilities.