Active online confidence boosting for efficient object classification
Mund D, Triebel R, Cremers D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2015-June
Pages Range: 1367-1373
Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation
Event location: Seattle, WA, USA
ISBN: 9781479969234
DOI: 10.1109/ICRA.2015.7139368
Abstract
We present a novel efficient algorithm for object classification. Our method is based on the active learning framework, in which training and classification are performed in loops, and new ground truth labels are queried from the supervisor in each loop. Our underlying classifier is from the family of boosting methods, but in contrast to earlier methods, our Confidence Boosting particularly focusses on misclassified samples that have a high classification confidence associated. We show that weighting these samples more than others leads to a decrease of overconfidence, for which we give a formal definition. As a result, our classifier is better suited for active learning, leading to steeper learning curves and less required label queries. We show the benefits of our approach on standard data sets from machine learning and robotics.
Involved external institutions
Germany (DE)How to cite
APA:
Mund, D., Triebel, R., & Cremers, D. (2015). Active online confidence boosting for efficient object classification. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 1367-1373). Seattle, WA, USA: Institute of Electrical and Electronics Engineers Inc..
MLA:
Mund, Dennis, Rudolph Triebel, and Daniel Cremers. "Active online confidence boosting for efficient object classification." Proceedings of the 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, Seattle, WA, USA Institute of Electrical and Electronics Engineers Inc., 2015. 1367-1373.
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