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.

本文提出了一种基于无监督分割的异常检测方法，使用多尺度区域特征生成器从预训练的深度卷积网络中生成描述子区域的特征，借助这些特征设计一个深度自编码器并通过快速特征重建对图像中的异常区域进行检测，该方法在多个基准数据集上取得了最新技术进展，并具有实际应用潜力。