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Asian Conference on Computer Vision

ACCV 2014: Computer Vision - ACCV 2014 Workshops pp 122–137Cite as

A Flexible Semi-supervised Feature Extraction Method for Image Classification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9010))

Abstract

This paper proposes a novel discriminant semi-supervised feature extraction for generic classification and recognition tasks. The paper has two main contributions. First, we propose a flexible linear semi-supervised feature extraction method that seeks a non-linear subspace that is close to a linear one. The proposed method is based on a criterion that simultaneously exploits the discrimination information provided by the labeled samples, maintains the graph-based smoothness associated with all samples, regularizes the complexity of the linear transform, and minimizes the discrepancy between the unknown linear regression and the unknown non-linear projection. Second, we provide extensive experiments on four benchmark databases in order to study the performance of the proposed method. These experiments demonstrate much improvement over the state-of-the-art algorithms that are either based on label propagation or semi-supervised graph-based embedding.

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Notes

  1. 1.

    http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html.

  2. 2.

    http://www.itl.nist.gov/iad/humanid/feret/.

  3. 3.

    http://www.facepix.org/.

  4. 4.

    http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php.

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Acknowledgment

This work was supported by the project EHU13/40.

Author information

Authors and Affiliations

  1. University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Fadi Dornaika & Youssof El Traboulsi

  2. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    Fadi Dornaika

Authors
  1. Fadi Dornaika
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  2. Youssof El Traboulsi
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Corresponding author

Correspondence to Fadi Dornaika .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C. V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Dornaika, F., El Traboulsi, Y. (2015). A Flexible Semi-supervised Feature Extraction Method for Image Classification. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-16634-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16633-9

  • Online ISBN: 978-3-319-16634-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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