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Affiliation

Pattern Recognition in Embedded Systems Group, Department of Computer Science, TU Dortmund Universtiy, 44227 Dortmund and Germany

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1 Papers

Unsupervised Learning of Scene Categories on the Lunar Surface

Topics: Categorization and Scene Understanding; Deep Learning for Visual Understanding

Authors: Thorsten Wilhelm ¹ , Rene Grzeszick ² , Gernot A. Fink ² and Christian Wöhler ¹

Affiliations: ¹ Image Analysis Group, Department of Electrical Engineering, TU Dortmund University, 44227 Dortmund and Germany , ² Pattern Recognition in Embedded Systems Group, Department of Computer Science, TU Dortmund Universtiy, 44227 Dortmund and Germany

Digital Object Identifier: 10.5220/0007569506140621

Proceedings of VISAPP 2019/VISIGRAPP, ISBN: 978-989-758-354-4

Quick Abstract

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.