Unsupervised image-based anomaly detection using multi-scale context-dependent deep autoencoding gaussian mixture model

A false alarm reduction method for reducing false alarms in an automatic defect detection system, the false alarm reduction method comprising:

• performing, by a processor, a cropping operation on each of input images at randomly chosen positions to form a set of cropped images of a same size at different scales in different contexts;
• encoding, by a first conditional deep autoencoder of a CONtext-conditional Deep Autoencoding Gaussian Mixture Model (CONDA-GMM), each of the cropped images without a respective center block as input for measuring a discrepancy between a reconstructed center block and the target center block, and
• encoding, by a second conditional deep autoencoder of the CONDA-GMM, an entirety of the cropped images with the target center block;
• constructing, by the CONDA-GMM, density estimates based on both reconstruction error features and low-dimensional embedding representations derived from encodings of the cropped images;
• and
• determining, by the processor, an existence of an anomaly based on a prediction of a likelihood of the anomaly existing in a framework of a context-dependent Conditional Gaussian Mixture Model (CGMM), given the context being a representation of the cropped image with the center block removed and having a discrepancy above a threshold amount.