Clustering of illustrations by atmosphere using a combination of
  supervised and unsupervised learning
Keisuke Kubota Masahiro Okuda
The distribution of illustrations on social media, such as Twitter and Pixiv
has increased with the growing popularity of animation, games, and animated
movies. The "atmosphere" of illustrations plays an important role in user
preferences. Classifying illustrations by atmosphere can be helpful for
recommendations and searches. However, assigning clear labels to the elusive
"atmosphere" and conventional supervised classification is not always
practical. Furthermore, even images with similar colors, edges, and low-level
features may not have similar atmospheres, making classification based on
low-level features challenging. In this paper, this problem is solved using
both supervised and unsupervised learning with pseudo-labels. The feature
vectors are obtained using the supervised method with pseudo-labels that
contribute to an ambiguous atmosphere. Further, clustering is performed based
on these feature vectors. Experimental analyses show that our method
outperforms conventional methods in human-like clustering on datasets manually
classified by humans.