Mutual Boosting 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 thus enabling information inference between parts and objects in a compositional hierarchy. In our experiments eye-, nose-, mouth- and face detectors are trained using the Mutual Boosting framework. Results show the method outperforms applications overlooking contextual information. We suggest that achieving contextual integration is a step toward human-like detection capabilities.

Authors

• Michael Fink
• Pietro Perona

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