  Book covers communicate information to potential readers, but can that same
information be learned by computers? We propose using a deep Convolutional
Neural Network (CNN) to predict the genre of a book based on the visual clues
provided by its cover. The purpose of this research is to investigate whether
relationships between books and their covers can be learned. However,
determining the genre of a book is a difficult task because covers can be
ambiguous and genres can be overarching. Despite this, we show that a CNN can
extract features and learn underlying design rules set by the designer to
define a genre. Using machine learning, we can bring the large amount of
resources available to the book cover design process. In addition, we present a
new challenging dataset that can be used for many pattern recognition tasks.
