Judging a Book By its Cover

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.

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Datasets


Introduced in the Paper:

Book Cover Dataset

Used in the Paper:

ImageNet
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Genre classification Book Cover Dataset AlexNet Top 1 Accuracy 24.7% # 1
Genre classification Book Cover Dataset LeNet Top 1 Accuracy 13.5% # 2

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