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The distribution of illustrations on social media, such as Twitter and Pixivhas increased with the growing popularity of animation, games, and animatedmovies. The "atmosphere" of illustrations plays an important role in userpreferences. Classifying illustrations by atmosphere can be helpful forrecommendations and searches. However, assigning clear labels to the elusive"atmosphere" and conventional supervised classification is not alwayspractical. Furthermore, even images with similar colors, edges, and low-levelfeatures may not have similar atmospheres, making classification based onlow-level features challenging. In this paper, this problem is solved usingboth supervised and unsupervised learning with pseudo-labels. The featurevectors are obtained using the supervised method with pseudo-labels thatcontribute to an ambiguous atmosphere. Further, clustering is performed basedon these feature vectors. Experimental analyses show that our methodoutperforms conventional methods in human-like clustering on datasets manuallyclassified by humans.

Clustering of illustrations by atmosphere using a combination of supervised and unsupervised learning