{"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/large-scale-classification-of-fine-art","title":"Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature","arxiv_id":"1505.00855","date":"2015-05-05","proceeding":null,"authors":["Babak Saleh","Ahmed Elgammal"],"abstract":"In the past few years, the number of fine-art collections that are digitized\nand publicly available has been growing rapidly. With the availability of such\nlarge collections of digitized artworks comes the need to develop multimedia\nsystems to archive and retrieve this pool of data. Measuring the visual\nsimilarity between artistic items is an essential step for such multimedia\nsystems, which can benefit more high-level multimedia tasks. In order to model\nthis similarity between paintings, we should extract the appropriate visual\nfeatures for paintings and find out the best approach to learn the similarity\nmetric based on these features. We investigate a comprehensive list of visual\nfeatures and metric learning approaches to learn an optimized similarity\nmeasure between paintings. We develop a machine that is able to make\naesthetic-related semantic-level judgments, such as predicting a painting's\nstyle, genre, and artist, as well as providing similarity measures optimized\nbased on the knowledge available in the domain of art historical\ninterpretation. Our experiments show the value of using this similarity measure\nfor the aforementioned prediction tasks.","url_abs":"http://arxiv.org/abs/1505.00855v1","url_pdf":"http://arxiv.org/pdf/1505.00855v1.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":"large-scale-classification-of-fine-art","repo_url":"https://github.com/cs-chan/Artwork-Synthesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"metric-learning","task_name":"Metric Learning"}],"methods":[],"datasets_introduced":[{"slug":"wikiart","name":"WikiArt","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1505.00855","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}