2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSMR-4.12
Paper Title DEEP UNSUPERVISED IMAGE ANOMALY DETECTION: AN INFORMATION THEORETIC FRAMEWORK
Authors Fei Ye, Shanghai Jiao Tong University, China; Huangjie Zheng, University of Texas at Austin, United States; Chaoqin Huang, Ya Zhang, Shanghai Jiao Tong University, China
SessionSMR-4: Image and Video Sensing, Modeling, and Representation
LocationArea F
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Sensing, Modeling, and Representation: Image & video representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consistent optimization direction with anomaly detection. In this paper, we return to a direct objective function for anomaly detection with information theory, which maximizes the distance between normal and anomalous data in terms of the joint distribution of images and their representation. To make this objective function directly optimizable under the unsupervised setting, we manage to find its lower bound which weights the trade-off between mutual information and entropy, which leads to a novel information theoretic framework for unsupervised image anomaly detection. Extensive experiments on several benchmark data sets have shown that the proposed framework significantly outperforms several state-of-the-arts.