{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/judging-a-book-by-its-cover","title":"Judging a Book By its Cover","arxiv_id":"1610.09204","date":"2016-10-28","proceeding":null,"authors":["Brian Kenji Iwana","Syed Tahseen Raza Rizvi","Sheraz Ahmed","Andreas Dengel","Seiichi Uchida"],"abstract":"Book covers communicate information to potential readers, but can that same\ninformation be learned by computers? We propose using a deep Convolutional\nNeural Network (CNN) to predict the genre of a book based on the visual clues\nprovided by its cover. The purpose of this research is to investigate whether\nrelationships between books and their covers can be learned. However,\ndetermining the genre of a book is a difficult task because covers can be\nambiguous and genres can be overarching. Despite this, we show that a CNN can\nextract features and learn underlying design rules set by the designer to\ndefine a genre. Using machine learning, we can bring the large amount of\nresources available to the book cover design process. In addition, we present a\nnew challenging dataset that can be used for many pattern recognition tasks.","url_abs":"http://arxiv.org/abs/1610.09204v3","url_pdf":"http://arxiv.org/pdf/1610.09204v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"judging-a-book-by-its-cover","repo_url":"https://github.com/uchidalab/book-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"judging-a-book-by-its-cover","repo_url":"https://github.com/SeaOfFrost/BookCoverClassifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"judging-a-book-by-its-cover","repo_url":"https://github.com/adamjeanlaurent/Book-Recommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"judging-a-book-by-its-cover","repo_url":"https://github.com/akshaybhatia10/Book-Genre-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"genre-classification","task_name":"Genre classification"}],"methods":[],"datasets_introduced":[{"slug":"book-cover-dataset","name":"Book Cover Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/genre-classification-on-book-cover-dataset","task":"Genre classification","dataset":"Book Cover Dataset","model":"AlexNet","rank_in_archive_order":1,"of":2,"metrics":{"Top 1 Accuracy":"24.7%"},"uses_additional_data":false},{"leaderboard":"/sota/genre-classification-on-book-cover-dataset","task":"Genre classification","dataset":"Book Cover Dataset","model":"LeNet","rank_in_archive_order":2,"of":2,"metrics":{"Top 1 Accuracy":"13.5%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1610.09204","atlas_url":"https://app.syntology.ai/?focus=1610.09204","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}