Skip to main content

Advertisement

Log in

Positive and unlabeled learning on generating strategy for weakly anomaly detection

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Anomalies are rare, contextual, and hard to annotate in anomaly detection scenarios. Usually, anomalies are coarse-grained labeled and there exists at least one abnormal patch in image and video segmentation. In such a setting, anomaly detection encounters anomaly diversity, quantity, and weakly-label problems. In this work, we propose a weakly anomaly detection framework by using Positive-unlabeled Learning (PUL) on generated weakly-labeled samples. This framework consists of sample generation module and anomaly detection module. In sample generation, Wasserstein Generative Adversarial Networks (WGAN) generates unlabeled patch-level samples including abnormal and normal classes. The unlabeled generated samples increase the quantity and diversity of anomalies for the PUL task. In anomaly detection model, a coarse-grained two-stage iterative PUL algorithm is proposed on positive labeled normal samples and unlabeled generated samples, which improves accuracy and stability. In the experiment, we evaluate our proposal in ablation study, which verifies the effectiveness of combining WGAN and PUL. Compared with typical anomaly detection models, our framework performs better with enhancements of 1.09% and 0.8% on the UCSD-Ped2 and Avenue datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data Availability

The research uses the MNIST, CIFAR-10, UCSD-Ped and Avenue datasets from computer vision standard datasets. Datasets are available upon request.

References

  1. Zhao, Y., Zheng, G., Mukherjee, S., McCann, R., Awadallah, A.: Admoe: Anomaly detection with mixture-of-experts from noisy labels. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 4937–4945 (2023)

  2. Khan, S.S., Madden, M.G.: One-class classification: taxonomy of study and review of techniques. Knowl. Eng. Rev. 29(3), 345–374 (2014)

    Article  MATH  Google Scholar 

  3. Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N.: Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:1510.01553 (2015)

  4. Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International Conference on Information Processing in Medical Imaging, pp. 146–157 (2017). Springer

  5. Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: Semi-supervised anomaly detection via adversarial training. In: Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, pp. 622–637 (2018). Springer

  6. Pourreza, M., Mohammadi, B., Khaki, M., Bouindour, S., Snoussi, H., Sabokrou, M.: G2d: generate to detect anomaly. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2003–2012 (2021)

  7. Denis, F.: Pac learning from positive statistical queries. In: Advanced Intelligent Systems, pp. 112–126 (1998). Springer

  8. Denis, F., Gilleron, R., Letouzey, F.: Learning from positive and unlabeled examples. Theoret. Comput. Sci. 348(1), 70–83 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Ul Amin, S., Kim, B., Jung, Y., Seo, S., Park, S.: Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3d convolutions and long short-term memory modules. Adv. Intell. Syst. 2300706 (2024)

  10. Niaz, A., Ul Amin, S., Soomro, S., Zia, H., Nam Choi, K.: Spatially aware fusion in 3d convolutional autoencoders for video anomaly detection. IEEE Access 12, 104770–104784 (2024). https://doi.org/10.1109/ACCESS.2024.3435144

    Article  Google Scholar 

  11. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  12. Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6479–6488 (2018)

  13. Zhang, J., Qing, L., Miao, J.: Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 4030–4034 (2019). IEEE

  14. Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J.W., Carneiro, G.: Weakly-supervised video anomaly detection with robust temporal feature magnitude learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4975–4986 (2021)

  15. Feng, J.-C., Hong, F.-T., Zheng, W.-S.: Mist: multiple instance self-training framework for video anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14009–14018 (2021)

  16. Hou, M., Chaib-Draa, B., Li, C., Zhao, Q.: Generative adversarial positive-unlabelled learning. arXiv preprint arXiv:1711.08054 (2017)

  17. Chiaroni, F., Rahal, M.-C., Hueber, N., Dufaux, F.: Learning with a generative adversarial network from a positive unlabeled dataset for image classification. In: 2018 25th IEEE International Conference on Image Processing (icip), pp. 1368–1372 (2018). IEEE

  18. Li, S., Liu, F., Jiao, L.: Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 1395–1403 (2022)

  19. Ul Amin, S., Ullah, M., Sajjad, M., Cheikh, F.A., Hijji, M., Hijji, A., Muhammad, K.: Eadn: an efficient deep learning model for anomaly detection in videos. Mathematics 10(9), 1555 (2022)

    Article  MATH  Google Scholar 

  20. Mahadevan, S., Shah, S.L.: Fault detection and diagnosis in process data using one-class support vector machines. J. Process Control 19(10), 1627–1639 (2009)

    Article  MATH  Google Scholar 

  21. Sotiris, V.A., Peter, W.T., Pecht, M.G.: Anomaly detection through a bayesian support vector machine. IEEE Trans. Reliab. 59(2), 277–286 (2010)

    Article  MATH  Google Scholar 

  22. Chong, Y.S., Tay, Y.H.: Abnormal event detection in videos using spatiotemporal autoencoder. In: Advances in Neural Networks-ISNN 2017: 14th International Symposium, pp. 189–196 (2017). Springer

  23. Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., Hua, X.-S.: Spatio-temporal autoencoder for video anomaly detection. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1933–1941 (2017)

  24. Elkan, C., Noto, K.: Learning classifiers from only positive and unlabeled data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213–220 (2008)

  25. Peng, T., Zuo, W., He, F.: SVM based adaptive learning method for text classification from positive and unlabeled documents. Knowl. Inf. Syst. 16, 281–301 (2008)

    Article  MATH  Google Scholar 

  26. Mordelet, F., Vert, J.-P.: A bagging SVM to learn from positive and unlabeled examples. Pattern Recogn. Lett. 37, 201–209 (2014)

    Article  MATH  Google Scholar 

  27. Li, C., Hua, X.-L.: Towards positive unlabeled learning for parallel data mining: a random forest framework. In: Advanced Data Mining and Applications: 10th International Conference, pp. 573–587 (2014). Springer

  28. Chen, X., Chen, W., Chen, T., Yuan, Y., Gong, C., Chen, K., Wang, Z.: Self-pu: self boosted and calibrated positive-unlabeled training. In: International Conference on Machine Learning, pp. 1510–1519 (2020). PMLR

  29. Kaboutari, A., Bagherzadeh, J., Kheradmand, F.: An evaluation of two-step techniques for positive-unlabeled learning in text classification. Int. J. Comput. Appl. Technol. Res 3(9), 592–594 (2014)

    MATH  Google Scholar 

  30. Li, D., Nie, X., Gong, R., Lin, X., Yu, H.: Multi-branch gan-based abnormal events detection via context learning in surveillance videos. IEEE Trans. Circuits Syst. Video Technol. 34(5), 3439–3450 (2024). https://doi.org/10.1109/TCSVT.2023.3325451

    Article  MATH  Google Scholar 

  31. Liang, J., Xiao, Y., Zhou, J.T., Yang, F., Li, T., Fang, Z.: \(\text{ C}^2\) net: content-dependent and-independent cross-attention network for anomaly detection in videos. Appl. Intell. 54(2), 1980–1996 (2024)

    Article  Google Scholar 

  32. Chen, D., Tantai, X., Chang, X., Tian, M., Jia, T.: Weakly supervised anomaly detection based on two-step cyclic iterative pu learning strategy. Neural Process. Lett. 54, 1–18 (2022)

    Article  Google Scholar 

  33. Liu, B., Lee, W.S., Yu, P.S., Li, X.: Partially supervised classification of text documents. In: The International Conference on Machine Learning, pp. 387–394 (2002)

  34. Perera, P., Nallapati, R., Xiang, B.: Ocgan: One-class novelty detection using gans with constrained latent representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898–2906 (2019)

  35. Tian, M., Guo, D., Cui, Y., Pan, X., Chen, S.: Improving auto-encoder novelty detection using channel attention and entropy minimization. In: Proceedings of the 2nd ACM International Conference on Multimedia in Asia, pp. 1–6 (2021)

  36. Fan, J., Zhang, Q., Zhu, J., Zhang, M., Yang, Z., Cao, H.: Robust deep auto-encoding gaussian process regression for unsupervised anomaly detection. Neurocomputing 376, 180–190 (2020)

    Article  MATH  Google Scholar 

  37. Kim, J., Grauman, K.: Observe locally, infer globally: a space-time mrf for detecting abnormal activities with incremental updates. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2928 (2009)

  38. Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: Conference on Computer Vision and Pattern Recognition, pp. 3449–3456 (2011). IEEE

  39. Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: IEEE 10th International Conference on Signal Processing Proceedings, pp. 1975–1981 (2010). IEEE

  40. Colque, R.V.H.M., Caetano, C., Andrade, M.T.L., Schwartz, W.R.: Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Trans. Circuits Syst. Video Technol. 27(3), 673–682 (2016)

  41. Luo, W., Liu, W., Gao, S.: Remembering history with convolutional lstm for anomaly detection. In: 2017 IEEE International Conference on Multimedia and Expo (ICME), pp. 439–444 (2017). IEEE

  42. Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked rnn framework. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

  43. Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y., Goh, R.S.M.: Anomalynet: an anomaly detection network for video surveillance. IEEE Trans. Inf. Forensics Secur. 14(10), 2537–2550 (2019)

    Article  Google Scholar 

Download references

Funding

This work is supported in part by the National Natural Science Foundation of China (62202087, 62206043 and 62173083); Guangdong Basic and Applied Basic Research Foundation (2024A1515010244); Fundamental Research Funds for the Central Universities (N2404011, N2404008).

Author information

Authors and Affiliations

Authors

Contributions

Shizhuo Deng: Writing—review & editing, Writing— original draft, Validation, Software, Methodology, Conceptualization. Bowen Han: Writing—review & editing, Software, Data curation. Xiaohong Li: Writing—review & editing, Validation, Software, Data curation. Siqi Lan: Software, Data curation. Dongyue Chen: Writing—review & editing, Methodology, Conceptualization. Tong Jia: Writing—review & editing, Conceptualization. Hao Wang: Software, Data curation.

Corresponding author

Correspondence to Dongyue Chen.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, S., Han, B., Li, X. et al. Positive and unlabeled learning on generating strategy for weakly anomaly detection. SIViP 19, 171 (2025). https://doi.org/10.1007/s11760-024-03797-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03797-8

Keywords

Navigation