  Automatic detecting anomalous regions in images of objects or textures
without priors of the anomalies is challenging, especially when the anomalies
appear in very small areas of the images, making difficult-to-detect visual
variations, such as defects on manufacturing products. This paper proposes an
effective unsupervised anomaly segmentation approach that can detect and
segment out the anomalies in small and confined regions of images. Concretely,
we develop a multi-scale regional feature generator that can generate multiple
spatial context-aware representations from pre-trained deep convolutional
networks for every subregion of an image. The regional representations not only
describe the local characteristics of corresponding regions but also encode
their multiple spatial context information, making them discriminative and very
beneficial for anomaly detection. Leveraging these descriptive regional
features, we then design a deep yet efficient convolutional autoencoder and
detect anomalous regions within images via fast feature reconstruction. Our
method is simple yet effective and efficient. It advances the state-of-the-art
performances on several benchmark datasets and shows great potential for real
applications.
