Huang, Y., Zhu, F., Shao, L. et al. (1 more author) (2016) Color object recognition via cross-domain learning on RGB-D images. In: Proceedings - IEEE International Conference on Robotics and Automation. 2016 IEEE International Conference on Robotics and Automation (ICRA), May 16th - 21st 2016, Stockholm, Sweden. IEEE , pp. 1672-1677. ISBN 9781467380263
Abstract
This paper addresses the object recognition problem using multiple-domain inputs. We present a novel approach that utilizes labeled RGB-D data in the training stage, where depth features are extracted for enhancing the discriminative capability of the original learning system that only relies on RGB images. The highly dissimilar source and target domain data are mapped into a unified feature space through transfer at both feature and classifier levels. In order to alleviate cross-domain discrepancy, we employ a state-of-the-art domain-adaptive dictionary learning algorithm that updates image representations in both domains and the classifier parameters simultaneously. The proposed method is trained on a RGB-D Object dataset and evaluated on the Caltech-256 dataset. Experimental results suggest that our approach can lead to significant performance gain over the state-of-the-art methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. This is an author produced version of a paper subsequently published in Proceedings of 2016 IEEE International Conference on Robotics and Automation (ICRA). Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Jul 2016 16:12 |
Last Modified: | 22 Mar 2018 13:37 |
Published Version: | http://dx.doi.org/10.1109/ICRA.2016.7487308 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICRA.2016.7487308 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:102859 |