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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2018: Trends and Applications in Knowledge Discovery and Data Mining pp 16–26Cite as

Unsupervised Domain Adaptation Dictionary Learning for Visual Recognition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11154))

Abstract

Over the last years, dictionary learning method has been extensively applied to deal with various computer vision recognition applications, and produced state-of-the-art results. However, when the data instances of a target domain have a different distribution than that of a source domain, the dictionary learning method may fail to perform well. In this paper, we address the cross-domain visual recognition problem and propose a simple but effective unsupervised domain adaptation approach, where labeled data are only from source domain. In order to bring the original data in source and target domain into the same distribution, the proposed method forcing nearest coupled data between source and target domain to have identical sparse representations while jointly learning dictionaries for each domain, where the learned dictionaries can reconstruct original data in source and target domain respectively. So that sparse representations of original data can be used to perform visual recognition tasks. We demonstrate the effectiveness of our approach on standard datasets. Our method performs on par or better than competitive state-of-the-art methods.

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Author information

Authors and Affiliations

  1. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, China

    Zhun Zhong, Zongmin Li, Runlin Li & Xiaoxia Sun

Authors
  1. Zhun Zhong
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  2. Zongmin Li
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  3. Runlin Li
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  4. Xiaoxia Sun
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Corresponding author

Correspondence to Zhun Zhong .

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Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Mohadeseh Ganji

  2. University of Melbourne, Melbourne, VIC, Australia

    Lida Rashidi

  3. McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

  4. Griffith University, Gold Coast, QLD, Australia

    Can Wang

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Zhong, Z., Li, Z., Li, R., Sun, X. (2018). Unsupervised Domain Adaptation Dictionary Learning for Visual Recognition. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-04503-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04502-9

  • Online ISBN: 978-3-030-04503-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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