Skip to main content
SpringerLink
Log in

Fast Distance-Based Anomaly Detection in Images Using an Inception-Like Autoencoder

  • Conference paper
  • First Online:
Discovery Science (DS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11828))

Included in the following conference series:

Abstract

The goal of anomaly detection is to identify examples that deviate from normal or expected behavior. We tackle this problem for images. We consider a two-phase approach. First, using normal examples, a convolutional autoencoder (CAE) is trained to extract a low-dimensional representation of the images. Here, we propose a novel architectural choice when designing the CAE, an Inception-like CAE. It combines convolutional filters of different kernel sizes and it uses a Global Average Pooling (GAP) operation to extract the representations from the CAE’s bottleneck layer. Second, we employ a distanced-based anomaly detector in the low-dimensional space of the learned representation for the images. However, instead of computing the exact distance, we compute an approximate distance using product quantization. This alleviates the high memory and prediction time costs of distance-based anomaly detectors. We compare our proposed approach to a number of baselines and state-of-the-art methods on four image datasets, and we find that our approach resulted in improved predictive performance.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/natasasdj/anomalyDetection.

References

  1. Andrews, J.T., Morton, E.J., Griffin, L.D.: Detecting anomalous data using auto-encoders. Int. J. Mach. Learn. Comput. 6(1), 21 (2016)

    Google Scholar 

  2. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. ACM SIGMOD Rec. 29(2), 93–104 (2000)

    Article  Google Scholar 

  3. Campos, G.O., et al.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Disc. 30(4), 891–927 (2016)

    Article  MathSciNet  Google Scholar 

  4. Chakravarty, P., Zhang, A.M., Jarvis, R., Kleeman, L.: Anomaly detection and tracking for a patrolling robot. In: Australasian Conference on Robotics and Automation (ACRA). Citeseer (2007)

    Google Scholar 

  5. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–72 (2009)

    Article  Google Scholar 

  6. Chollet, F., et al.: Keras (2015). https://keras.io

  7. Creusot, C., Munawar, A.: Real-time small obstacle detection on highways using compressive RBM road reconstruction. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 162–167. IEEE (2015)

    Google Scholar 

  8. Deecke, L., Vandermeulen, R., Ruff, L., Mandt, S., Kloft, M.: Anomaly Detection with Generative Adversarial Networks (2018)

    Google Scholar 

  9. Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit. 58, 121–134 (2016)

    Article  Google Scholar 

  10. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of 25th International Conference on Very Large Data Bases, pp. 518–529 (1999)

    Google Scholar 

  11. Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations. In: Advances in Neural Information Processing Systems, vol. 31, pp. 9781–9791 (2018)

    Google Scholar 

  12. Hachiya, H., Matsugu, M.: NSH: normality sensitive hashing for anomaly detection. In: IEEE International Conference on Computer Vision Workshops, pp. 795–802 (2013)

    Google Scholar 

  13. Haselmann, M., Gruber, D.P., Tabatabai, P.: Anomaly detection using deep learning based image completion. In: Proceedings of 17th IEEE ICMLA, pp. 1237–1242 (2018)

    Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  15. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  16. Jégou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117–128 (2011)

    Article  Google Scholar 

  17. Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017)

  18. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  19. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical Report, Citeseer (2009)

    Google Scholar 

  20. LeCun, Y., Cortes, C., Burges, C.J.: MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist

  21. Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)

  22. Munawar, A., Vinayavekhin, P., De Magistris, G.: Spatio-temporal anomaly detection for industrial robots through prediction in unsupervised feature space. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1017–1025 (2017)

    Google Scholar 

  23. Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: ACM Sigmod Record, vol. 29, pp. 427–438. ACM (2000)

    Google Scholar 

  24. Richter, C., Roy, N.: Safe Visual Navigation via Deep Learning and Novelty Detection (2017)

    Google Scholar 

  25. Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4390–4399 (2018)

    Google Scholar 

  26. Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R.: Fully convolutional neural network for fast anomaly detection in crowded scenes. arXiv preprint arXiv:1609.00866 (2016)

  27. Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. arXiv preprint arXiv:1802.09088 (2018)

  28. Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the 2nd Workshop on Machine Learning for Sensory Data Analysis, p. 4. ACM (2014)

    Google Scholar 

  29. Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12

    Chapter  Google Scholar 

  30. Schubert, E., Zimek, A., Kriegel, H.-P.: Fast and scalable outlier detection with approximate nearest neighbor ensembles. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M.A. (eds.) DASFAA 2015. LNCS, vol. 9050, pp. 19–36. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18123-3_2

    Chapter  Google Scholar 

  31. Seeböck, P., et al.: Identifying and categorizing anomalies in retinal imaging data. arXiv preprint arXiv:1612.00686 (2016)

  32. Shashikar, S., Upadhyaya, V.: Traffic surveillance and anomaly detection using image processing. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6. IEEE (2017)

    Google Scholar 

  33. 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)

    Google Scholar 

  34. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  35. Taboada-Crispi, A., Sahli, H., Hernandez-Pacheco, D., Falcon-Ruiz, A.: Anomaly detection in medical image analysis. In: Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications, pp. 426–446 (2009)

    Chapter  Google Scholar 

  36. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)

    Article  Google Scholar 

  37. Vercruyssen, V., Meert, W., Verbruggen, G., Maes, K., Baumer, R., Davis, J.: Semi-supervised anomaly detection with an application to water analytics. In: IEEE 2018 International Conference on Data Mining, pp. 527–536 (2018)

    Google Scholar 

  38. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164 (2015)

    Google Scholar 

  39. Wei, Q., Ren, Y., Hou, R., Shi, B., Lo, J.Y., Carin, L.: Anomaly detection for medical images based on a one-class classification. In: Medical Imaging 2018: Computer-Aided Diagnosis, vol. 10575, p. 105751M. International Society for Optics and Photonics (2018)

    Google Scholar 

  40. Xia, Y., Cao, X., Wen, F., Hua, G., Sun, J.: Learning discriminative reconstructions for unsupervised outlier removal. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1511–1519 (2015)

    Google Scholar 

  41. Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)

  42. 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)

  43. Zhai, S., Cheng, Y., Lu, W., Zhang, Z.: Deep structured energy based models for anomaly detection. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, vol. 48, pp. 1100–1109 (2016)

    Google Scholar 

  44. Zhanga, Y., Lua, H., Zhanga, L., Ruanb, X., Sakaib, S.: Video anomaly detection based on locality sensitive hashing filters. Pattern Recognit. 59, 302–311 (2016)

    Article  Google Scholar 

  45. Zong, B., et al.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)

    Google Scholar 

Download references

Acknowledgements

We thank Lukas Ruff from TU Berlin for help reproducing the results from [25]. This research has been partially funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 752907. JD is partially supported by KU Leuven Research Fund (C14/17/07, C32/17/036), Research Foundation - Flanders (EOS No. 30992574, G0D8819N), VLAIO-SBO grant HYMOP (150033), and the Flanders AI Impulse Program.

Author information

Authors and Affiliations

  1. IRIS Technology Solutions, Barcelona, Spain

    Natasa Sarafijanovic-Djukic

  2. KU Leuven, Leuven, Belgium

    Jesse Davis

Authors
  1. Natasa Sarafijanovic-Djukic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jesse Davis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natasa Sarafijanovic-Djukic .

Editor information

Editors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Petra Kralj Novak

  2. Rudjer Bošković Institute, Zagreb, Croatia

    Tomislav Šmuc

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Sašo Džeroski

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sarafijanovic-Djukic, N., Davis, J. (2019). Fast Distance-Based Anomaly Detection in Images Using an Inception-Like Autoencoder. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33778-0_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33777-3

  • Online ISBN: 978-3-030-33778-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation