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Multi-label Learning with Missing Labels Based on Instance-Wise and Label-Wise Correlations for Image Classification

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 88))

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Abstract

In recent years, multi-label learning has been widely concerned, especially in the fields of image annotation and classification. However, the problem of missing labels is still an urgent issue to be addressed in this domain. To alleviate the missing labels issue, we propose a multi-label learning algorithm with missing labels for image classification, which makes effective use of instance-wise correlation and label-wise correlation. First, we adopt the weighted least square loss to impel the consistency between the estimated labels and the provided labels. Then we utilize the linear reconstruction strategy to explore the instance-wise correlation, and we also utilize the low rank representation (LRR) to investigate the label-wise correlation. Finally, the Laplacian manifold regularization is applied to incorporate the loss function and the above two correlations. To validate the effectiveness of the proposed algorithm, we compare the proposed algorithm with other four state-of-art methods on three popular image datasets. The experimental results show that our algorithm can achieve a much better performance.

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Acknowledgment

This work was supported in part by the Natural Science Foundation of China under Grant 61972060 and Grant U1713213, and in part by the Natural Science Foundation of Chongqing under Grant cstc2019cxcyljrc-td0270 and Grant cstc2019jcyj-cxttX0002.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, China

    Weisheng Li, Yuexin Zhu & Ying Lu

Authors
  1. Weisheng Li
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  2. Yuexin Zhu
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  3. Ying Lu
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Corresponding author

Correspondence to Weisheng Li .

Editor information

Editors and Affiliations

  1. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Hongying Meng

  2. School of Electronical Information and Artificial Engineering, Shaanxi University of Science and Technology, Xi’an, China

    Tao Lei

  3. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Maozhen Li

  4. College of Electrical and Information, Hunan University, Changsha, China

    Kenli Li

  5. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Li, W., Zhu, Y., Lu, Y. (2021). Multi-label Learning with Missing Labels Based on Instance-Wise and Label-Wise Correlations for Image Classification. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_8

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