Boosted Cross-Domain Dictionary Learning for Visual Categorization

Fan Zhu, Ling Shao, Yi Fang

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number7436612
Pages (from-to)6-18
Number of pages13
JournalIEEE Intelligent Systems
Volume31
Issue number3
DOIs
StatePublished - May 1 2016

Keywords

  • boosting
  • dictionary learning
  • intelligent systems
  • transfer learning
  • visual categorization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Boosted Cross-Domain Dictionary Learning for Visual Categorization'. Together they form a unique fingerprint.

Cite this