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CAAI Artificial Intelligence Research 2022, 1(1): 79-92 https://doi.org/10.26599/AIR.2022.9150006
Open Access | Issue | Published: 28 August 2022

Multi-Label Image Classification with Weak Correlation Prior

Show Author's Information Xiao Ouyang1,  Ruidong Fan1,  Hong Tao1( ),  Chenping Hou1( )
Department of Systems Science, National University of Defense Technology, Changsha 410073, China
Keywords:
image recognition, label correlation, multi-label classification, weakly-supervised learning
Cite this article:
Ouyang X, Fan R, Tao H, et al. Multi-Label Image Classification with Weak Correlation Prior. CAAI Artificial Intelligence Research, 2022, 1(1): 79-92. https://doi.org/10.26599/AIR.2022.9150006
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Abstract Full text About this article

Image classification is vital and basic in many data analysis domains. Since real-world images generally contain multiple diverse semantic labels, it amounts to a typical multi-label classification problem. Traditional multi-label image classification relies on a large amount of training data with plenty of labels, which requires a lot of human and financial costs. By contrast, one can easily obtain a correlation matrix of concerned categories in current scene based on the historical image data in other application scenarios. How to perform image classification with only label correlation priors, without specific and costly annotated labels, is an important but rarely studied problem. In this paper, we propose a model to classify images with this kind of weak correlation prior. We use label correlation to recapitulate the sample similarity, employ the prior information to decompose the projection matrix when regressing the label indication matrix, and introduce the ℓ2,1 norm to select features for each image. Finally, experimental results on several image datasets demonstrate that the proposed model has distinct advantages over current state-of-the-art multi-label classification methods.


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About this article

Multi-Label Image Classification with Weak Correlation Prior

Show Author's information Xiao Ouyang1, Ruidong Fan1, Hong Tao1( ), Chenping Hou1( )
Department of Systems Science, National University of Defense Technology, Changsha 410073, China

Abstract

Image classification is vital and basic in many data analysis domains. Since real-world images generally contain multiple diverse semantic labels, it amounts to a typical multi-label classification problem. Traditional multi-label image classification relies on a large amount of training data with plenty of labels, which requires a lot of human and financial costs. By contrast, one can easily obtain a correlation matrix of concerned categories in current scene based on the historical image data in other application scenarios. How to perform image classification with only label correlation priors, without specific and costly annotated labels, is an important but rarely studied problem. In this paper, we propose a model to classify images with this kind of weak correlation prior. We use label correlation to recapitulate the sample similarity, employ the prior information to decompose the projection matrix when regressing the label indication matrix, and introduce the ℓ2,1 norm to select features for each image. Finally, experimental results on several image datasets demonstrate that the proposed model has distinct advantages over current state-of-the-art multi-label classification methods.

Keywords: image recognition, label correlation, multi-label classification, weakly-supervised learning

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Received: 19 April 2022
Revised: 14 August 2022
Accepted: 20 August 2022
Published: 28 August 2022
Issue date: September 2022

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© The author(s) 2022

Acknowledgements

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61922087, 61906201, 62006238, and 62136005), and the Natural Science Fund for Distinguished Young Scholars of Hunan Province (No. 2019JJ20020).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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