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A Novel \(\ell ^1\)-graph Based Image Classification Algorithm

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

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Abstract

In original sparse representation based classification algorithms, each training sample belongs to exactly one class, neglecting the association between the training sample and the other classes. However, different classes’ features are visually similar and correlated (e.g. facial images), which means the association between the training sample and the different classes contain important information, and must be taken into consideration. In this paper, we propose a novel \(\ell ^1\)-graph based image classification algorithm (LGC). Our algorithm can automatically calculate associations between training samples and all classes, which are used for future classification. We evaluate our method on some popular visual benchmarks, the experimental results prove the effectiveness of our method.

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Acknowledgments

This research is supported in part by the Major State Basic Research Development Program of China (973 Program, 2012CB315803), the National Natural Science Foundation of China (61371078), and the Research Fund for the Doctoral Program of Higher Education of China (20130002110051).

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Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, People’s Republic of China

    Jia-Yue Xu & Shu-Tao Xia

Authors
  1. Jia-Yue Xu
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  2. Shu-Tao Xia
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Corresponding author

Correspondence to Jia-Yue Xu .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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Xu, JY., Xia, ST. (2015). A Novel \(\ell ^1\)-graph Based Image Classification Algorithm. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-26532-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

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