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. https://resolver.caltech.edu/CaltechAUTHORS:20160309-110000460
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Abstract
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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Additional Information: | © 2004 Massachusetts Institute of Technology. | ||||||
Series Name: | Advances in Neural Information Processing Systems | ||||||
Issue or Number: | 16 | ||||||
Record Number: | CaltechAUTHORS:20160309-110000460 | ||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20160309-110000460 | ||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
ID Code: | 65243 | ||||||
Collection: | CaltechAUTHORS | ||||||
Deposited By: | INVALID USER | ||||||
Deposited On: | 14 Mar 2016 23:53 | ||||||
Last Modified: | 03 Oct 2019 09:45 |
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