{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:22:35Z","timestamp":1771953755453,"version":"3.50.1"},"reference-count":19,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition Letters"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1016\/j.patrec.2019.11.008","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T11:34:26Z","timestamp":1573212866000},"page":"56-62","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":35,"special_numbering":"C","title":["Learning the Principles of Art History with convolutional neural networks"],"prefix":"10.1016","volume":"129","author":[{"given":"Eva","family":"Cetinic","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8037-8198","authenticated-orcid":false,"given":"Tomislav","family":"Lipic","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0802-3288","authenticated-orcid":false,"given":"Sonja","family":"Grgic","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.patrec.2019.11.008_bib0001","series-title":"Principles of Art History","author":"W\u00f6lfflin","year":"1950"},{"key":"10.1016\/j.patrec.2019.11.008_bib0002","series-title":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, February 2\u20137, 2018","article-title":"The shape of art history in the eyes of the machine","author":"Elgammal","year":"2018"},{"key":"10.1016\/j.patrec.2019.11.008_bib0003","series-title":"British Machine Vision Conference, BMVC 2014, Nottingham, UK, September 1\u20135, 2014","article-title":"Recognizing image style","author":"Karayev","year":"2014"},{"key":"10.1016\/j.patrec.2019.11.008_bib0004","series-title":"Computer Vision - ECCV 2014 Workshops - Zurich, Switzerland, September 6\u20137 and 12, 2014, Proceedings, Part I","first-page":"71","article-title":"Classification of artistic styles using binarized features derived from a deep neural network","author":"Bar","year":"2014"},{"key":"10.1016\/j.patrec.2019.11.008_bib0005","series-title":"Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6\u20139, 2016, Proceedings, Part II","first-page":"20","article-title":"Deeppainter: painter classification using deep convolutional autoencoders","author":"David","year":"2016"},{"key":"10.1016\/j.patrec.2019.11.008_bib0006","series-title":"2014\u202fIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23\u201328, 2014","first-page":"580","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014"},{"key":"10.1016\/j.patrec.2019.11.008_bib0007","series-title":"2016\u202fIEEE International Conference on Image Processing, ICIP 2016, Phoenix, AZ, USA, September 25\u201328, 2016","first-page":"3693","article-title":"Fine tuning CNNs with scarce training data adapting imagenet to art epoch classification","author":"Hentschel","year":"2016"},{"issue":"7","key":"10.1016\/j.patrec.2019.11.008_bib0008","doi-asserted-by":"crossref","first-page":"3565","DOI":"10.1007\/s11042-014-2193-x","article-title":"Toward automated discovery of artistic influence","volume":"75","author":"Saleh","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patrec.2019.11.008_bib0009","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.eswa.2018.07.026","article-title":"Fine-tuning convolutional neural networks for fine art classification","volume":"114","author":"Cetinic","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patrec.2019.11.008_bib0010","series-title":"Computer Vision - ECCV 2016 Workshops - Amsterdam, The Netherlands, October 8\u201310 and 15\u201316, 2016, Proceedings, Part I","first-page":"753","article-title":"Visual link retrieval in a database of paintings","author":"Seguin","year":"2016"},{"issue":"4","key":"10.1016\/j.patrec.2019.11.008_bib0011","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MSP.2015.2406955","article-title":"Toward discovery of the artist\u2019s style: learning to recognize artists by their artworks","volume":"32","author":"van Noord","year":"2015","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.patrec.2019.11.008_bib0012","series-title":"Proceedings of International Conference on Multimedia Retrieval","first-page":"451","article-title":"The Rijksmuseum challenge: museum-centered visual recognition","author":"Mensink","year":"2014"},{"issue":"4","key":"10.1016\/j.patrec.2019.11.008_bib0013","first-page":"88","article-title":"Omniart: a large-scale artistic benchmark","volume":"14","author":"Strezoski","year":"2018","journal-title":"ACM Trans. Multimedia Comput.Commun. Appl. (TOMM)"},{"key":"10.1016\/j.patrec.2019.11.008_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.05.036","article-title":"Multitask painting categorization by deep multibranch neural network","author":"Bianco","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patrec.2019.11.008_bib0015","series-title":"Computer Vision - ECCV 2014 Workshops - Zurich, Switzerland, September 6\u20137 and 12, 2014, Proceedings, Part I","first-page":"54","article-title":"In search of art","author":"Crowley","year":"2014"},{"key":"10.1016\/j.patrec.2019.11.008_bib0016","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3389\/fpsyg.2017.00830","article-title":"Using cnn features to better understand what makes visual artworks special","volume":"8","author":"Brachmann","year":"2017","journal-title":"Front. Psychol."},{"key":"10.1016\/j.patrec.2019.11.008_bib0017","doi-asserted-by":"crossref","first-page":"73694","DOI":"10.1109\/ACCESS.2019.2921101","article-title":"A deep learning perspective on beauty, sentiment, and remembrance of art","volume":"7","author":"Cetinic","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.patrec.2019.11.008_bib0018","series-title":"Proceedings of the 22nd ACM International Conference on Multimedia","first-page":"675","article-title":"Caffe: convolutional architecture for fast feature embedding","author":"Jia","year":"2014"},{"key":"10.1016\/j.patrec.2019.11.008_bib0019","series-title":"Advances in neural information processing systems","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"}],"container-title":["Pattern Recognition Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865519303228?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865519303228?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T06:07:50Z","timestamp":1759126070000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167865519303228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":19,"alternative-id":["S0167865519303228"],"URL":"https:\/\/doi.org\/10.1016\/j.patrec.2019.11.008","relation":{},"ISSN":["0167-8655"],"issn-type":[{"value":"0167-8655","type":"print"}],"subject":[],"published":{"date-parts":[[2020,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Learning the Principles of Art History with convolutional neural networks","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patrec.2019.11.008","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}