A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection
Abstract
Detecting the anomalous information in multimedia is valuable to many computer vision applications. Recently, many pixel-wise methods modeling by deep learning model have been presented, which can be divided in reconstruction-based and distance-based methods. However, reconstruction-based methods suffer from the low precision of pixel reconstructions. Distance-based methods extract the hierarchical features by a pre-trained model, in order to estimate the anomalies by distances between normal and anomalous features. Nevertheless, multi-level features are ignored in these methods, and semantic information is not considered which is important to enhance the description of anomalies. To over-come the problems, we propose a novel semantic-enhanced anomaly detection method based on deep Support Vector Data Description (SVDD). A new semantic correlation module (SCB) is introduced to enhance the semantic information of the feature representations by cosine similarity. Mean-while, the multi-level architecture is utilized to estimate the final pixel-wise anomaly score. Experimental results demonstrate the proposed method outperforms state-of-the-art methods on MVTec and STC dataset.
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Deep Industrial Image Anomaly Detection: A Survey
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“SE-SVDD [21] SVDD ResNet The paper proposes a Semantic Correlation module (SCB) to represent abnormal semantics information.”
Influential
- Authors
- Hu, C., Chen, K., & Shao, H.
- Affiliations
- University of Shanghai for Science and Technology (CN)
- Venue
- 2021 IEEE International Conference on Multimedia and Expo (ICME)
- Publisher
- IEEE
- Published
- 2021-07-05
- Pages
- 1-6
- OpenAlex
- W3167437959
- Cited by
- 24
- References
- 29
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 10 · 2023: 5 · 2022: 2
- FWCI
- 1.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Multimedia and Expo (ICME) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- Deep SVDD, cosine similarity, multi-level feature representation, image anomaly detection
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