Thorsten Wilhelm, Rene Grzeszick, Gernot A. Fink and Christian W{\"o}hler
Proc. International Conference on Computer Vision Theory and Applications (VISAPP), 2019.
Prague, Czech Republic
Learning scene categories is a challenging task due to the high diversity of images. State-of-the-art methods are typically trained in a fully supervised manner, requiring manual labeling effort. In some cases, however, these manual labels are not available. In this work, an example of completely unlabeled scene images, where labels are hardly obtainable, is presented: orbital images of the lunar surface. A novel method that exploits feature representations derived from a CNN trained on a different data source is presented. These features are adapted to the lunar surface in an unsupervised manner, allowing for learning scene categories and detecting regions of interest. The experiments show that meaningful representatives and scene categories can be derived in a fully unsupervised fashion.