Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on unsupervised anomaly detection and localization while requiring virtually no training time.

[1]  Abhijit Mahalanobis,et al.  Attention Guided Anomaly Localization in Images , 2020, ECCV.

[2]  Thomas G. Dietterich,et al.  Deep Anomaly Detection with Outlier Exposure , 2018, ICLR.

[3]  Carsten Steger,et al.  MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Paolo Napoletano,et al.  Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity , 2018, Sensors.

[6]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[7]  Alexander Binder,et al.  Deep One-Class Classification , 2018, ICML.

[8]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[9]  Yedid Hoshen,et al.  Deep Nearest Neighbor Anomaly Detection , 2020, ArXiv.

[10]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[11]  Abhijit Mahalanobis,et al.  Attention Guided Anomaly Detection and Localization in Images , 2019, ArXiv.

[12]  Adam Finkelstein,et al.  PatchMatch: a randomized correspondence algorithm for structural image editing , 2009, SIGGRAPH 2009.

[13]  Yedid Hoshen,et al.  Classification-Based Anomaly Detection for General Data , 2020, ICLR.

[14]  Bo Zong,et al.  Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.

[15]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[16]  Alexei A. Efros,et al.  Colorful Image Colorization , 2016, ECCV.

[17]  Cewu Lu,et al.  Inverse-Transform AutoEncoder for Anomaly Detection , 2019, ArXiv.

[18]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[19]  Toby P. Breckon,et al.  GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training , 2018, ACCV.

[20]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[21]  Marius Kloft,et al.  Image Anomaly Detection with Generative Adversarial Networks , 2018, ECML/PKDD.

[22]  Mary W. Green.  The appropriate and effective use of security technologies in U.S. schools : a guide for schools and law enforcement agencies. , 1999 .

[23]  Pietro Perona,et al.  Caltech-UCSD Birds 200 , 2010 .

[24]  Dawn Song,et al.  A Benchmark for Anomaly Segmentation , 2019, ArXiv.

[25]  Jonghyun Choi,et al.  Learning Temporal Regularity in Video Sequences , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Salvatore J. Stolfo,et al.  A Geometric Framework for Unsupervised Anomaly Detection , 2002, Applications of Data Mining in Computer Security.

[27]  Svetha Venkatesh,et al.  Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[28]  Chandan Srivastava,et al.  Support Vector Data Description , 2011 .

[29]  Shenghua Gao,et al.  A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[30]  Cewu Lu,et al.  Attribute Restoration Framework for Anomaly Detection , 2019, IEEE Transactions on Multimedia.

[31]  Paul Bergmann,et al.  Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Ran El-Yaniv,et al.  Deep Anomaly Detection Using Geometric Transformations , 2018, NeurIPS.

[33]  See-Kiong Ng,et al.  Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series , 2018, ArXiv.

[34]  Aleksandar Lazarevic,et al.  Outlier Detection with Kernel Density Functions , 2007, MLDM.

[35]  Nikos Komodakis,et al.  Unsupervised Representation Learning by Predicting Image Rotations , 2018, ICLR.

[36]  Chen Shen,et al.  Spatio-Temporal AutoEncoder for Video Anomaly Detection , 2017, ACM Multimedia.

[37]  Takehisa Yairi,et al.  Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction , 2014, MLSDA'14.

[38]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.