  Understanding how humans communicate and perceive narratives is important for
media technology research and development. This is particularly important in
current times when there are tools and algorithms that are easily available for
amateur users to create high-quality content. Narrative media develops over
time a set of recognizable patterns of features across similar artifacts. Genre
is one such grouping of artifacts for narrative media with similar patterns,
tropes, and story structures. While much work has been done on genre-based
classifications in text and video, we present a novel approach to do a
multi-modal analysis of genre based on comics and manga-style visual
narratives. We present a systematic feature analysis of an annotated dataset
that includes a variety of western and eastern visual books with annotations
for high-level narrative patterns. We then present a detailed analysis of the
contributions of high-level features to genre classification for this medium.
We highlight some of the limitations and challenges of our existing
computational approaches in modeling subjective labels. Our contributions to
the community are: a dataset of annotated manga books, a multi-modal analysis
of visual panels and text in a constrained and popular medium through
high-level features, and a systematic process for incorporating subjective
narrative patterns in computational models.
