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
J. Zhang, Q. Wu, C. Shen, J. Zhang, and J. Lu, Multilabel image classification with regional latent semantic dependencies, IEEE Trans. Multim., vol. 20, no. 10, pp. 2801–2813, 2018.
S. Wen, W. Liu, Y. Yang, P. Zhou, Z. Guo, Z. Yan, Y. Chen, and T. Huang, Multilabel image classification via feature/label co-projection, IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 11, pp. 7250–7259, 2021.
Y. Xia, L. Nie, L. Zhang, Y. Yang, R. Hong, and X. Li, Weakly supervised multilabel clustering and its applications in computer vision, IEEE Trans. Cybern., vol. 46, no. 12, pp. 3220–3232, 2016.
R. Cabral, F. De la Torre, J. P. Costeira, and A. Bernardino, Matrix completion for weaklysupervised multi-label image classification, IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 1, pp. 121–135, 2015.
L. Sun, G. Lyu, S. Feng, and X. Huang, Beyond missing: Weakly-supervised multi-label learning with incomplete and noisy labels, Appl. Intell., vol. 51, no. 3, pp. 1552–1564, 2021.
B. Frenay and M. Verleysen, Classification in the presence of label noise: A Survey, IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 5, pp. 845–869, 2014.
J. Wu, A. Guo, V. S. Sheng, P. Zhao, and Z. Cui, An active learning approach for multi-label image classification with sample noise, Int. J. Pattern Recognit. Artif. Intell., vol. 32, no. 3, p. 1850005, 2018.
H. Guo, L. Han, S. Su, and Z. Sun, Deep multi-instance multi-label learning for image annotation, Int. J. Pattern Recognit. Artif. Intell., vol. 32, no. 3, p. 1859005, 2018.
M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, Learning multi-label scene classification, Pattern Recognit., vol. 37, no. 9, pp. 1757–1771, 2004.
J. Shan, C. Hou, H. Tao, W. Zhuge, and D. Yi, Co-learning binary classifiers for LP-based multi-label classification, Cogn. Syst. Res., vol. 55, pp. 146–152, 2019.
M. L. Zhang and Z. H. Zhou, ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognit., vol. 40, no. 7, pp. 2038–2048, 2007.
Y. Wei, W. Xia, M. Lin, J. Huang, B. Ni, J. Dong, Y. Zhao, and S. Yan, HCP: A flexible CNN framework for multi-label image classification, IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 9, pp. 1901–1907, 2016.
M. L. Zhang and J. P. Fang, Partial multi-label learning via credible label elicitation, IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 10, pp. 3587–3599, 2021.
H. Hu, R. Wang, F. Nie, X. Yang, and W. Yu, Fast unsupervised feature selection with anchor graph and
H. Tao, C. Hou, F. Nie, Y. Jiao, and D. Yi, Effective discriminative feature selection with nontrivial solution, IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 4, pp. 796–808, 2016.
M. L. Zhang, J. P. Fang, and Y. B. Wang, BiLabel-specific features for multi-label classification, ACM Trans. Knowl. Discov. Data, vol. 16, no. 1, p. 18, 2022.
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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