  This paper examines, if it is possible to learn structural invariants of city
images by using only a single reference picture when producing transformations
along the variants in the dataset. Previous work explored the problem of
learning from only a few examples and showed that data augmentation techniques
benefit performance and generalization for machine learning approaches. First a
principal component analysis in conjunction with a Fourier transform is trained
on a single reference augmentation training dataset using the city images.
Secondly a convolutional neural network is trained on a similar dataset with
more samples. The findings are that the convolutional neural network is capable
of finding images of the same category whereas the applied principal component
analysis in conjunction with a Fourier transform failed to solve this task.
