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

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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
Uncontrolled Keywords: boosting, dictionary learning, intelligent systems, transfer learning, visual categorization
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 15 Jul 2016 13:15
Last Modified: 12 Oct 2019 22:52

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