  In this paper, we suggest a way, how to use SIFT and SURF algorithms to
extract the image features for anomaly detection. We use those feature vectors
to train various classifiers on a real-world dataset in the semi -supervised
(with a small number of faulty samples) manner with a large number of
classifiers and in the one-class (with no faulty samples) manner using the SVDD
and SVM classifier. We prove, that the SIFT and SURF algorithms could be used
as feature extractors, that they could be used to train a semi-supervised and
one-class classifier with an accuracy around 89\% and that the performance of
the one-class classifier could be comparable to the semi-supervised one. We
also made our dataset and source code publicly available.
