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Pacific Rim Conference on Multimedia

PCM 2018: Advances in Multimedia Information Processing – PCM 2018 pp 808–818Cite as

Discrete Semi-supervised Multi-label Learning for Image Classification

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11164))

Abstract

Multi-label image classification is a critical problem in semantic based image processing. Traditional semi-supervised multi-label learning methods usually learn classification functions in continuous label space. And the ignorance of discrete constraint of semantic labels impedes the classification performance. In this paper, we specifically consider the discrete constraint and propose Discrete Semi-supervised Multi-label Learning (DSML) for image classification. In DSML, we propose a semi-supervised framework with discrete constraint. Then we introduce anchor graph learning to improve the scalability, and derive an ADMM based alternating optimization process to solve the framework. Experimental results demonstrate the superiorly of DSML compared with several advanced semi-supervised methods.

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References

  1. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples (2006). JMLR.org

  2. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2010)

    Article  Google Scholar 

  3. Bruzzone, L., Chi, M., Marconcini, M.: A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans. Geosci. Remote Sens. 44(11), 3363–3373 (2006)

    Article  Google Scholar 

  4. Cheng, Z., Shen, J., Zhu, L., Kankanhalli, M., Nie, L.: Exploiting music play sequence for music recommendation. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3654–3660 (2017)

    Google Scholar 

  5. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceedings of the ACM International Conference on Image and Video Retrieval, p. 48. ACM (2009)

    Google Scholar 

  6. Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes challenge 2007 (voc 2007) results (2007). http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html (2008)

  7. Guillaumin, M., Mensink, T., Verbeek, J., Schmid, C.: TagProp: discriminative metric learning in nearest neighbor models for image auto-annotation. In: IEEE International Conference on Computer Vision, pp. 309–316 (2009)

    Google Scholar 

  8. Guillaumin, M., Verbeek, J., Schmid, C.: Multimodal semi-supervised learning for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 902–909. IEEE (2010)

    Google Scholar 

  9. Hajinezhad, D., Chang, T.H., Wang, X., Shi, Q., Hong, M.: Nonnegative matrix factorization using ADMM: algorithm and convergence analysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4742–4746 (2016)

    Google Scholar 

  10. Huiskes, M.J., Lew, M.S.: The MIR Flickr retrieval evaluation. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 39–43. ACM (2008)

    Google Scholar 

  11. Jing, L., Shen, C., Yang, L., Yu, J., Ng, M.K.: Multi-label classification by semi-supervised singular value decomposition. IEEE Trans. Image Process. 26(10), 4612–4625 (2017)

    Article  MathSciNet  Google Scholar 

  12. Liu, W.: Hashing with graphs. In: Proceedings of International Conference on Machine Learning, pp. 1–8, June 2011

    Google Scholar 

  13. Liu, W., Mu, C., Kumar, S., Chang, S.F.: Discrete graph hashing. In: Advances in Neural Information Processing Systems, pp. 3419–3427 (2014)

    Google Scholar 

  14. Luo, Y., Tao, D., Geng, B., Xu, C., Maybank, S.J.: Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Trans. Image Process. 22(2), 523–536 (2013)

    Article  MathSciNet  Google Scholar 

  15. Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: Computer Vision and Pattern Recognition, pp. 37–45 (2015)

    Google Scholar 

  16. Xie, L., Pan, P., Lu, Y., Wang, S.: A cross-modal multi-task learning framework for image annotation. In: ACM International Conference on Conference on Information and Knowledge Management, pp. 431–440 (2014)

    Google Scholar 

  17. Xie, L., Shen, J., Han, J., Zhu, L., Shao, L.: Dynamic multi-view hashing for online image retrieval. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 3133–3139 (2017)

    Google Scholar 

  18. Yang, Y., Shen, F., Huang, Z., Shen, H.T., Li, X.: Discrete nonnegative spectral clustering. IEEE Trans. Knowl. Data Eng. PP(99), 1 (2017)

    Google Scholar 

  19. Yang, Y., Wu, F., Nie, F., Shen, H.T., Zhuang, Y., Hauptmann, A.G.: Web and personal image annotation by mining label correlation with relaxed visual graph embedding. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 21(3), 1339–51 (2012)

    Article  MathSciNet  Google Scholar 

  20. Yi, L., Rong, J., Liu, Y.: Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, 16–20 July 2006, Boston, Massachusetts, USA, pp. 421–426 (2006)

    Google Scholar 

  21. Zhang, H., Shen, F., Liu, W., He, X., Luan, H., Chua, T.S.: Discrete collaborative filtering. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325–334 (2016)

    Google Scholar 

  22. Zhou, Y.H., Zhou, Z.H.: Large margin distribution learning with cost interval and unlabeled data. IEEE Trans. Knowl. Data Eng. 28(7), 1749–1763 (2016)

    Article  Google Scholar 

  23. Zhu, L., Huang, Z., Chang, X., Song, J., Shen, H.T.: Exploring consistent preferences: discrete hashing with pair-exemplar for scalable landmark search, pp. 726–734 (2017)

    Google Scholar 

  24. Zhu, L., Huang, Z., Li, Z., Xie, L., Shen, H.T.: Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–13 (2018). https://doi.org/10.1109/TNNLS.2018.2797248

    Article  Google Scholar 

  25. Zhu, L., Huang, Z., Liu, X., He, X., Sun, J., Zhou, X.: Discrete multimodal hashing with canonical views for robust mobile landmark search. IEEE Trans. Multimedia 19(9), 2066–2079 (2017)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61702388) and the Fundamental Research Funds for the Central Universities (WUT: 2018IVB021).

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Authors and Affiliations

  1. Department of Mathematics, Wuhan University of Technology, Wuhan, China

    Liang Xie, Lang He, Haohao Shu & Shengyuan Hu

Authors
  1. Liang Xie
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  2. Lang He
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  3. Haohao Shu
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  4. Shengyuan Hu
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Corresponding author

Correspondence to Liang Xie .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

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Xie, L., He, L., Shu, H., Hu, S. (2018). Discrete Semi-supervised Multi-label Learning for Image Classification. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_74

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  • DOI: https://doi.org/10.1007/978-3-030-00776-8_74

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00775-1

  • Online ISBN: 978-3-030-00776-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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