  Images of line drawings are generally composed of primitive elements. One of
the most fundamental elements to characterize images is the topology; line
segments belong to a category different from closed circles, and closed circles
with different winding degrees are nonequivalent. We investigate images with
nontrivial winding using the unsupervised machine learning. We build an
autoencoder model with a combination of convolutional and fully connected
neural networks. We confirm that compressed data filtered from the trained
model retain more than 90% of correct information on the topology, evidencing
that image clustering from the unsupervised learning features the topology.
