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Wavelet-SVDD: Anomaly Detection and Segmentation with Frequency Domain Attention

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14177))

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Abstract

Anomaly detection is a formidable challenge that entails the formulation of a model capable of detecting anomalous patterns in datasets, even when anomalous data points are absent. Traditional algorithms focused on learning knowledge regarding the typical features that arise in images, such as texture, shape, and color, to distinguish between normal and anomalous examples. However, there is untapped potential in frequency domain features for differentiating anomalous patterns, and current methodologies have not exhaustively exploited this avenue. In this work, we present an extension of the deep learning version of support vector data description (SVDD), a prevalent algorithm used for anomaly detection, through the introduction of Wavelet transformation and frequency domain attentions in the feature learning network. This extension allows for the consideration of frequency domain patterns in defect detection, and improves detection performance significantly. We performed extensive experiments on the MVTecAD dataset, and the results revealed that our approach attained advanced performance in both anomaly detection and segmentation localization, thereby confirming the efficacy of our proposed innovative designs.

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References

  1. Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Trans. Image Process. 1(2), 205–220 (1992)

    Article  Google Scholar 

  2. Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad-a comprehensive real-world dataset for unsupervised anomaly detection. In: ICCV, pp. 9592–9600 (2019)

    Google Scholar 

  3. Calders, T., Jaroszewicz, S.: Efficient AUC optimization for classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 42–53. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74976-9_8

    Chapter  Google Scholar 

  4. Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: ICCV, pp. 1422–1430 (2015)

    Google Scholar 

  5. Fujieda, S., Takayama, K., Hachisuka, T.: Wavelet convolutional neural networks. arXiv preprint arXiv:1805.08620 (2018)

  6. Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. In: CVPR, pp. 1–8 (2008)

    Google Scholar 

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

  8. Li, C.-L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: self-supervised learning for anomaly detection and localization. In: CVPR, pp. 9664–9674 (2021)

    Google Scholar 

  9. Li, Q., Shen, L., Guo, S., Lai, Z.: Wavelet integrated CNNs for noise-robust image classification. In: CVPR, pp. 7245–7254 (2020)

    Google Scholar 

  10. Ruff, L., Vandermeulen, R., Goernitz, N., et al.: Deep one-class classification. In: ICML, pp. 4393–4402 (2018)

    Google Scholar 

  11. Tao, R., Zhao, X., Li, W., et al.: Hyperspectral anomaly detection by fractional Fourier entropy. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 12(12), 4920–4929 (2019)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  13. Wang, H., Wu, X., Huang, Z., Xing, E.P.: High-frequency component helps explain the generalization of convolutional neural networks. In: CVPR, pp. 8684–8694 (2020)

    Google Scholar 

  14. Wu, T., Wen, M., Wang, Y., et al.: Spectra-difference based anomaly-detection for infrared hyperspectral dim-moving-point-target detection. Infrared Phys. Technol. 128, 104489 (2023)

    Article  Google Scholar 

  15. Yi, J., Yoon, S.: Patch SVDD: patch-level SVDD for anomaly detection and segmentation. In: ACCV (2020)

    Google Scholar 

  16. Zhao, X., Huang, P., Shu, X.: Wavelet-attention CNN for image classification. Multimedia Syst. 28(3), 915–924 (2022)

    Article  Google Scholar 

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Acknowledgements

This work is jointly supported by National Natural Science Foundation of China(62106290) and Program for Innovation Research in Central University of Finance and Economics.

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Correspondence to Weiyu Guo .

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Zhou, L., Guo, W., Cao, J., Zhang, X., Wang, Y. (2023). Wavelet-SVDD: Anomaly Detection and Segmentation with Frequency Domain Attention. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_16

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  • DOI: https://doi.org/10.1007/978-3-031-46664-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46663-2

  • Online ISBN: 978-3-031-46664-9

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