基于离散-连续特征耦合的图像异常检测算法

The purpose of optical image anomaly detection is to train the model only with normal samples and detect abnormal samples that deviate from the normal law. To solve the universal reconstruction and low^-quality interference problems in the generation^-based anomaly detection algorithm, a new image anomaly detection algorithm is proposed based on the autoencoder network. First, the latent features are transformed into continuous and discrete features, namely block descriptive and hash features. Hash features have binarization characteristics; it can avoid under^-sampling of latent space, thereby the problem of universal reconstruction can be effectively solved. Second, Based on the coupling relationship of discrete^-continuous features, the graph shrinkage method is used to establish the block similarity matrix which constructs the association between hash and description features. Then the interblock reconstruction method is proposed to ensure high-quality reconstruction of the image and solving the problem of low^-quality interference. Experiments on the international public dataset, MVTec AD, prove that the accuracy of the proposed algorithm is better than the present anomaly detection algorithms.