Color object recognition via cross-domain learning on RGB-D images

Huang, Yawen, Zhu, Fan, Shao, Ling and Frangi, Alejandro (2016) Color object recognition via cross-domain learning on RGB-D images. In: IEEE International Conference on Robotics and Automation (ICRA), 16-21 May 2016, Stockholm.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 05 Aug 2016 08:48
Last Modified: 12 Oct 2019 12:13

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