Judging a Book By its Cover

Abstract

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.

6 pages, 9 figures

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Learning in Neural Networks: An Overview

Cited by in corpus (7)

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• What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels
• HyperSTAR: Task-Aware Hyperparameters for Deep Networks
• Deep multi-modal networks for book genre classification based on its cover
• How do Convolutional Neural Networks Learn Design?
• Font Style that Fits an Image -- Font Generation Based on Image Context
• Effect of Text Color on Word Embeddings