How do Convolutional Neural Networks Learn Design?
This work addresses the problem of interpreting visual design for book covers, which is incremental as it applies existing methods to a new domain.
The paper tackles the problem of understanding design principles in book cover images by using CNNs to predict genres and applying Layer-wise Relevance Propagation to highlight visual cues that distinguish genres, achieving insights into genre-specific design elements.
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.