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

Huang, Yawen, Zhu, Fan, Shao, Ling and Frangi, Alejandro F. (2016) Color object recognition via cross-domain learning on RGB-D images. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). The Institute of Electrical and Electronics Engineers (IEEE), SWE, pp. 1672-1677.

Full text not available from this repository.


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: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 07 Feb 2017 02:43
Last Modified: 22 Oct 2022 00:00
DOI: 10.1109/ICRA.2016.7487308

Actions (login required)

View Item View Item