  In this paper, we aim to understand the design principles in book cover
images which are carefully crafted by experts. Book covers are designed in a
unique way, specific to genres which convey important information to their
readers. By using Convolutional Neural Networks (CNN) to predict book genres
from cover images, visual cues which distinguish genres can be highlighted and
analyzed. In order to understand these visual clues contributing towards the
decision of a genre, we present the application of Layer-wise Relevance
Propagation (LRP) on the book cover image classification results. We use LRP to
explain the pixel-wise contributions of book cover design and highlight the
design elements contributing towards particular genres. In addition, with the
use of state-of-the-art object and text detection methods, insights about
genre-specific book cover designs are discovered.
