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Mutual Boosting for Contextual Inference

Fink, Michael and Perona, Pietro (2004) Mutual Boosting for Contextual Inference. In: Advances in Neural Information Processing Systems 16 (NIPS 2003). Advances in Neural Information Processing Systems. No.16. MIT Press , Cambridge, MA, pp. 1515-1522. ISBN 0-262-20152-6.

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

Item Type:Book Section
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Perona, Pietro0000-0002-7583-5809
Additional Information:© 2004 Massachusetts Institute of Technology.
Series Name:Advances in Neural Information Processing Systems
Issue or Number:16
Record Number:CaltechAUTHORS:20160309-110000460
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65243
Deposited On:14 Mar 2016 23:53
Last Modified:03 Oct 2019 09:45

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