Boosted cross-domain dictionary learning for visual categorization

Zhu, Fan, Shao, Ling and Fang, Yi (2016) Boosted cross-domain dictionary learning for visual categorization. IEEE Intelligent Systems, 31 (3). pp. 6-18. ISSN 1541-1672

Full text not available from this repository.


In an extension of the AdaBoost and transfer AdaBoost algorithms, a boosted cross-domain categorization framework works with a learned domain-adaptive dictionary pair and boosted classifiers so that both the auxiliary domain data representations and their distributions are optimized to match the target domain. By iteratively updating weak classifiers, the categorization system allocates more credits to "similar"' auxiliary domain samples, while abandoning "dissimilar" auxiliary domain samples. The authors evaluated the proposed approach using multiple transfer learning scenarios, including image classification, human action recognition, and 3D object recognition. The proposed method consistently outperformed the state-of-the-art methods in all the evaluated scenarios.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 08 Mar 2017 01:41
Last Modified: 22 Oct 2022 02:10
DOI: 10.1109/MIS.2016.30

Actions (login required)

View Item View Item