Spherical regularized support vector description for visual anomaly detection

Anomaly detection is an important task in the computer vision, such as medical, security. One of the challenges in anomaly detection is not easy to obtain large-scale annotated anomalous data. Existing methods focus on one-class classification and weakly supervised learning. Deep support vector data Description (Deep SVDD) is an important method to realize one-class anomaly detection. However, previous Deep SVDD often encounter the hypersphere collapse when constructing the model of the hypersphere. To solve this problem, support vector data description based on spherical regularization (SR-SVDD) is proposed in this paper. SR-SVDD applies the idea of support vectors to optimize the learning process by introducing slack terms. Furthermore, this paper proposes weakly supervised support vector data description based on spherical regularization (SR-WSVDD), which utilizes small amounts of labeled data. Ablation experiments and comparison experiments are carried out on MNIST and CIFAR-10. Experimental results show that, compared with supervised algorithms, the performance of SR-WSVDD is improved by 3.7% on the MNIST, and 16.7% on the CIFAR-10. In addition, compared with other weakly supervised algorithms, SR-WSVDD improves by 1.8% on CIFAR-10 dataset. The proposed SR-SVDD solves the spherical collapse of previous Deep SVDD, and makes the anomaly detection results more accurate. © 2024 Science Press. All rights reserved.