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Semi-supervised multi-view binary learning for large-scale image clustering

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Abstract

Large-scale image clustering has attracted sustained attention in machine learning. The traditional methods based on real value representation often suffer from the data storage and calculation. To deal with these problems, the methods based on the binary representation and the multi-view learning are introduced recently. However, how to improve the clustering performance is still a challenge. Considering that one can obtain in prior parts of labels in many cases, we further develop the label information in the multi-view binary learning. This information is beneficial to the design of the involved similarity matrix, which plays an important part in the clustering problem. As a result, a new method is proposed, i.e., Semi-supervised Multi-view Binary Learning(SMBL). It is tested by using four benchmark data sets and compared with several commonly used large-scale and semi-supervised clustering approaches. The extensive experimental results show that the proposed method achieves superior performance.

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Notes

  1. https://archive.ics.uci.edu/ml/datasets/Multiple+Features

  2. http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html

  3. https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html

  4. https://www.cs.toronto.edu/kriz/cifar.html

  5. https://github.com/sepehrband/drawNemenyi

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Acknowledgements

This work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Grants 2019B010154002, 2019B010118001, and 2019B010121001; in part by the National Natural Science Foundation of China under Grants 61803096, 61801133, and U191140003; in part by the Guangzhou Science and Technology Program Project under Grant 202002030289; in part by the Natural Science Foundation of Guangdong Province under Grant 2020A1515010768 and Grant 2022A1515010688.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Mingyang Liu, Zuyuan Yang, Junhang Chen & Weijun Sun

  2. Ante Laser Co., Ltd., Guangzhou, 510663, China

    Zuyuan Yang

  3. School of Electrical Engineering, Guangzhou Railway Polytechnic, Guangzhou, 510430, Guangdong, China

    Wei Han

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  1. Mingyang Liu
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  2. Zuyuan Yang
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  3. Wei Han
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  4. Junhang Chen
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  5. Weijun Sun
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Correspondence to Wei Han.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Liu, M., Yang, Z., Han, W. et al. Semi-supervised multi-view binary learning for large-scale image clustering. Appl Intell 52, 14853–14870 (2022). https://doi.org/10.1007/s10489-022-03205-z

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