{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:04:22Z","timestamp":1776888262083,"version":"3.51.2"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>With the increase in massive digitized datasets of cultural artefacts, social and cultural scientists have an unprecedented opportunity for the discovery and expansion of cultural theory. The WikiArt dataset is one such example, with over 250,000 high quality images of historically significant artworks by over 3000 artists, ranging from the 15th century to the present day; it is a rich source for the potential mining of patterns and differences among artists, genres, and styles. However, such datasets are often difficult to analyse and use for answering complex questions of cultural evolution and divergence because of their raw formats as image files, which are represented as multi-dimensional tensors\/matrices. Recent developments in machine learning, multi-modal data analysis and image processing, however, open the door for us to create representations of images that extract important, domain-specific features from images. Art historians have long emphasised the importance of art style, and the colors used in art, as ways to characterise and retrieve art across genre, style, and artist. In this paper, we release a massive vector-based dataset of paintings (WikiArtVectors), with style representations and color distributions, which provides cultural and social scientists with a framework and database to explore relationships across these two vital dimensions. We use state-of-the-art deep learning and human perceptual color distributions to extract the representations for each painting, and aggregate them across artist, style, and genre. These vector representations and distributions can then be used in tandem with information-theoretic and distance metrics to identify large-scale patterns across art style, genre, and artist. We demonstrate the consistency of these vectors, and provide early explorations, while detailing future work and directions. All of our data and code is publicly available on GitHub.<\/jats:p>","DOI":"10.3390\/e24091175","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T21:05:12Z","timestamp":1661288712000},"page":"1175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures"],"prefix":"10.3390","volume":"24","author":[{"given":"Bhargav","family":"Srinivasa Desikan","sequence":"first","affiliation":[{"name":"Computer and Communication Sciences, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), 1015 Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hajime","family":"Shimao","sequence":"additional","affiliation":[{"name":"Department of Economics, McGill University, Montreal, QC H3A 0G4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0896-1167","authenticated-orcid":false,"given":"Helena","family":"Miton","sequence":"additional","affiliation":[{"name":"Santa Fe Institute, Santa Fe, NM 87501, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"ref_1","unstructured":"(2022, June 09). WikiArt. Available online: https:\/\/www.wikiart.org\/en\/about."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pirrone, R., Cannella, V., Gambino, O., Pipitone, A., and Russo, G. (December, January 30). Wikiart: An ontology-based information retrieval system for arts. Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy.","DOI":"10.1109\/ISDA.2009.219"},{"key":"ref_3","unstructured":"Mohammad, S., and Kiritchenko, S. (2018, January 7\u201312). Wikiart emotions: An annotated dataset of emotions evoked by art. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan."},{"key":"ref_4","first-page":"781","article-title":"Toward a postdigital humanities: Cultural analytics and the computational turn to data-driven scholarship","volume":"85","author":"Hall","year":"2013","journal-title":"Am. Lit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/01973762.2013.761106","article-title":"Is there a \u201cdigital\u201d art history?","volume":"29","author":"Drucker","year":"2013","journal-title":"Vis. Resour."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"E8585","DOI":"10.1073\/pnas.1800083115","article-title":"History of art paintings through the lens of entropy and complexity","volume":"115","author":"Sigaki","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107864","DOI":"10.1016\/j.patcog.2021.107864","article-title":"Automatic analysis of artistic paintings using information-based measures","volume":"114","author":"Silva","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 6\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Elgammal, A., Liu, B., Kim, D., Elhoseiny, M., and Mazzone, M. (2018, January 2\u20137). The shape of art history in the eyes of the machine. Proceedings of the The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11894"},{"key":"ref_10","first-page":"1","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","unstructured":"Elgammal, A., and Saleh, B. (July, January 29). Quantifying Creativity in Art Networks. Proceedings of the Sixth International Conference on Computational Creativity, Park City, UT, USA."},{"key":"ref_12","unstructured":"Kim, D., Xu, J., Elgammal, A., and Mazzone, M. (2019, January 17\u201321). Computational analysis of content in fine art paintings. Proceedings of the Tenth International Conference on Computational Creativity, Charlotte, NC, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1057\/s41599-021-00838-2","article-title":"Dynamics of artistic style: A computational analysis of the Maker\u2019s motoric qualities in a clay-relief practice","volume":"8","author":"Dick","year":"2021","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2307723.2307726","article-title":"Computer analysis of art","volume":"5","author":"Shamir","year":"2012","journal-title":"J. Comput. Cult. Herit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhuravleva, O., Savhalova, N., Komarov, A., Zherdev, D., Demina, A., Nesterov, A., and Nikonorov, A. (2021, January 20\u201324). Computational Analysis of the Aesthetic Content Relating to the Fine-Art Image. Proceedings of the 2021 International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia.","DOI":"10.1109\/ITNT52450.2021.9649042"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102","DOI":"10.3389\/fncom.2017.00102","article-title":"Computational and experimental approaches to visual aesthetics","volume":"11","author":"Brachmann","year":"2017","journal-title":"Front. Comput. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15-1","DOI":"10.2352\/ISSN.2470-1173.2021.14.CVAA-015","article-title":"Computational identification of significant actors in paintings through symbols and attributes","volume":"14","author":"Stork","year":"2021","journal-title":"Electron. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1167\/16.12.326","article-title":"A Neural Algorithm of Artistic Style","volume":"16","author":"Gatys","year":"2016","journal-title":"J. Vis."},{"key":"ref_19","unstructured":"Strezoski, G., and Worring, M. (2017). Omniart: Multi-task deep learning for artistic data analysis. arXiv."},{"key":"ref_20","unstructured":"Mao, H., Cheung, M., and She, J. (October, January 23\u2013). Deepart: Learning joint representations of visual arts. Proceedings of the 25th ACM international conference on Multimedia, Mountain, CA, USA."},{"key":"ref_21","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":"Hendriks","year":"2015","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tan, W.R., Chan, C.S., Aguirre, H.E., and Tanaka, K. (2016, January 25\u201328). Ceci n\u2019est pas une pipe: A deep convolutional network for fine-art paintings classification. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533051"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"191569","DOI":"10.1098\/rsos.191569","article-title":"Modelling and forecasting art movements with CGANs","volume":"7","author":"Lisi","year":"2020","journal-title":"R. Soc. Open Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Matsuo, S., and Yanai, K. (2016, January 6\u20139). CNN-based style vector for style image retrieval. Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, New York, NY, USA.","DOI":"10.1145\/2911996.2912057"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/TVCG.2019.2921336","article-title":"Neural style transfer: A review","volume":"26","author":"Jing","year":"2019","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104306","DOI":"10.1016\/j.cognition.2020.104306","article-title":"Color associations in abstract semantic domains","volume":"201","author":"Guilbeault","year":"2020","journal-title":"Cognition"},{"key":"ref_27","unstructured":"Desikan, B.S., Hull, T., Nadler, E.O., Guilbeault, D., Kar, A.A., Chu, M., and Sardo, D.R.L. (2020, January 8\u201313). comp-syn: Perceptually Grounded Word Embeddings with Color. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1002\/col.22211","article-title":"Computational color analysis of paintings for different artists of the XVI and XVII centuries","volume":"43","author":"Romero","year":"2018","journal-title":"Color Res. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.1364\/AO.378659","article-title":"Computing the relevant colors that describe the color palette of paintings","volume":"59","author":"Nieves","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"A170","DOI":"10.1364\/JOSAA.33.00A170","article-title":"Statistics of colors in paintings and natural scenes","volume":"33","author":"Montagner","year":"2016","journal-title":"JOSA A"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nieves, J.L., Ojeda, J., G\u00f3mez-Robledo, L., and Romero, J. (2021). Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings. J. Imaging, 7.","DOI":"10.3390\/jimaging7040072"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111","DOI":"10.55630\/sjc.2008.2.111-136","article-title":"Analysis of the distributions of color characteristics in art painting images","volume":"2","author":"Ivanova","year":"2008","journal-title":"Serdica J. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Puthenputhussery, A., Liu, Q., and Liu, C. (2016, January 7\u201310). Color multi-fusion fisher vector feature for fine art painting categorization and influence analysis. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477619"},{"key":"ref_34","first-page":"73","article-title":"Computer Vision Applications for Art History: Reflections and paradigms for future research","volume":"2021","author":"Foka","year":"2021","journal-title":"Proc. Eva Lond."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15131","DOI":"10.1364\/OE.25.015131","article-title":"Perceptually uniform color space for image signals including high dynamic range and wide gamut","volume":"25","author":"Safdar","year":"2017","journal-title":"Opt. Express"},{"key":"ref_36","first-page":"1","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chanwimalueang, T., and Mandic, D.P. (2017). Cosine similarity entropy: Self-correlation-based complexity analysis of dynamical systems. Entropy, 19.","DOI":"10.3390\/e19120652"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Karjus, A., Sol\u00e0, M.C., Ohm, T., Ahnert, S.E., and Schich, M. (2022). Compression ensembles quantify aesthetic complexity and the evolution of visual art. arXiv.","DOI":"10.1140\/epjds\/s13688-023-00397-3"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1126\/science.1240064","article-title":"A network framework of cultural history","volume":"345","author":"Schich","year":"2014","journal-title":"Science"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4607","DOI":"10.1073\/pnas.1717729115","article-title":"Individuals, institutions, and innovation in the debates of the French Revolution","volume":"115","author":"Barron","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.evolhumbehav.2013.01.004","article-title":"How portraits turned their eyes upon us: Visual preferences and demographic change in cultural evolution","volume":"34","author":"Morin","year":"2013","journal-title":"Evol. Hum. Behav."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"184","DOI":"10.15195\/v9.a8","article-title":"Cohort Succession Explains Most Change in Literary Culture","volume":"9","author":"Underwood","year":"2022","journal-title":"Sociol. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0049124117729703","article-title":"Computational grounded theory: A methodological framework","volume":"49","author":"Nelson","year":"2020","journal-title":"Sociol. Methods Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/9\/1175\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:14:13Z","timestamp":1760141653000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/9\/1175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["e24091175"],"URL":"https:\/\/doi.org\/10.3390\/e24091175","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,23]]}}}