Judging a Book By its Cover
This work addresses the challenge of automating genre classification for book covers, which could aid in design processes, but it is incremental as it applies an existing method to a new dataset.
The researchers tackled the problem of predicting a book's genre from its cover using a deep Convolutional Neural Network, showing that the model can learn design rules to extract features despite the ambiguity of covers and genres.
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.