{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:02:58Z","timestamp":1774022578299,"version":"3.50.1"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["PON AIM 1852414"],"award-info":[{"award-number":["PON AIM 1852414"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Universit\u00e0 degli Studi di Bari Aldo Moro"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture.<\/jats:p>","DOI":"10.1007\/s00521-021-05893-z","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T19:02:31Z","timestamp":1617390151000},"page":"12263-12282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6489-8628","authenticated-orcid":false,"given":"Giovanna","family":"Castellano","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0883-2691","authenticated-orcid":false,"given":"Gennaro","family":"Vessio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"5893_CR1","doi-asserted-by":"crossref","unstructured":"Carneiro G, da Silva NP, Del Bue A, Costeira JP (2012) Artistic image classification: an analysis on the printart database. In: European Conference on Computer Vision. Springer, pp 143\u2212157","DOI":"10.1007\/978-3-642-33765-9_11"},{"issue":"6","key":"5893_CR2","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1007\/s00138-014-0621-6","volume":"25","author":"FS Khan","year":"2014","unstructured":"Khan FS, Beigpour S, Van de Weijer J, Felsberg M (2014) Painting-91: a large scale database for computational painting categorization. Mach Vis Appl 25(6):1385\u22121397","journal-title":"Mach Vis Appl"},{"issue":"2","key":"5893_CR3","first-page":"8","volume":"7","author":"L Shamir","year":"2010","unstructured":"Shamir L, Macura T, Orlov N, Eckley DM, Goldberg IG (2010) Impressionism, expressionism, surrealism: automated recognition of painters and schools of art. ACM Trans Appl Percept (TAP) 7(2):8","journal-title":"ACM Trans Appl Percept (TAP)"},{"key":"5893_CR4","unstructured":"Arora RS, Elgammal A (2012) Towards automated classification of fine-art painting style: a comparative study. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp 3541\u22123544"},{"issue":"8","key":"5893_CR5","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u22121828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7553","key":"5893_CR6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2212444","journal-title":"Nature"},{"key":"5893_CR7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","volume":"234","author":"W Liu","year":"2017","unstructured":"Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11\u221226","journal-title":"Neurocomputing"},{"key":"5893_CR8","doi-asserted-by":"crossref","unstructured":"Castellano G, Vessio G (2021) A brief overview of deep learning approaches to pattern extraction and recognition in paintings and drawings. In: Pattern recognition. ICPR International workshops and challenges: virtual event, January 10\u201315, 2021, Proceedings, Part III, Springer International Publishing, pp 487\u2013501","DOI":"10.1007\/978-3-030-68796-0_35"},{"key":"5893_CR9","doi-asserted-by":"crossref","unstructured":"Mao H, Cheung M, She J (2017) Deepart: learning joint representations of visual arts. In: Proceedings of the 25th ACM International Conference on Multimedia. ACM, pp 1183\u22121191","DOI":"10.1145\/3123266.3123405"},{"key":"5893_CR10","doi-asserted-by":"crossref","unstructured":"Westlake N, Cai H, Hall P (2016) Detecting people in artwork with CNNs. In: European Conference on Computer Vision. Springer, pp 825\u2212841","DOI":"10.1007\/978-3-319-46604-0_57"},{"key":"5893_CR11","doi-asserted-by":"crossref","unstructured":"Wilber MJ, Fang C, Jin H, Hertzmann A, Collomosse J, Belongie S (2017) BAM! The Behance artistic media dataset for recognition beyond photography. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1202\u22121211","DOI":"10.1109\/ICCV.2017.136"},{"key":"5893_CR12","doi-asserted-by":"crossref","unstructured":"Shen X, Efros AA, Aubry M (2019) Discovering visual patterns in art collections with spatially-consistent feature learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9278\u20139287","DOI":"10.1109\/CVPR.2019.00950"},{"key":"5893_CR13","doi-asserted-by":"crossref","unstructured":"Garcia N, Vogiatzis G (2018) How to read paintings: semantic art understanding with multi-modal retrieval. In: Proceedings of the European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-030-11012-3_52"},{"key":"5893_CR14","doi-asserted-by":"crossref","unstructured":"Stefanini M, Cornia M, Baraldi L, Corsini M, Cucchiara R (2019) Artpedia: a new visual-semantic dataset with visual and contextual sentences in the artistic domain. In: International conference on image analysis and processing. Springer, pp 729\u2212740","DOI":"10.1007\/978-3-030-30645-8_66"},{"key":"5893_CR15","unstructured":"Mohammad S, Kiritchenko S (2018) Wikiart emotions: an annotated dataset of emotions evoked by art. In: Proceedings of the eleventh international conference on Language Resources and Evaluation (LREC 2018)"},{"issue":"4","key":"5893_CR16","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/5326.897072","volume":"30","author":"GP Zhang","year":"2000","unstructured":"Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 30(4):451\u2212462","journal-title":"IEEE Trans Syst Man Cybern Part C (Appl Rev)"},{"issue":"4","key":"5893_CR17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115\u2212133","journal-title":"Bull Math Biophys"},{"issue":"6","key":"5893_CR18","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386\u2212408","journal-title":"Psychol Rev"},{"issue":"6088","key":"5893_CR19","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2212536","journal-title":"Nature"},{"issue":"4","key":"5893_CR20","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2212551","journal-title":"Neural Comput"},{"issue":"8","key":"5893_CR21","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u22121780","journal-title":"Neural Comput"},{"issue":"3","key":"5893_CR22","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2212252","journal-title":"Int J Comput Vis"},{"key":"5893_CR23","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u22121105"},{"issue":"6","key":"5893_CR24","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","volume":"19","author":"R Miotto","year":"2018","unstructured":"Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 19(6):1236\u22121246","journal-title":"Brief Bioinform"},{"issue":"5","key":"5893_CR25","first-page":"851","volume":"18","author":"S Min","year":"2017","unstructured":"Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851\u2212869","journal-title":"Brief Bioinform"},{"issue":"3","key":"5893_CR26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3190618","volume":"51","author":"K Sundararajan","year":"2018","unstructured":"Sundararajan K, Woodard DL (2018) Deep learning for biometrics: a survey. ACM Comput Surv (CSUR) 51(3):1\u221234","journal-title":"ACM Comput Surv (CSUR)"},{"key":"5893_CR27","doi-asserted-by":"crossref","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","volume":"6","author":"Y Xin","year":"2018","unstructured":"Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Gao M, Hou H, Wang C (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 6:35365\u221235381","journal-title":"IEEE Access"},{"issue":"10","key":"5893_CR28","first-page":"1995","volume":"3361","author":"Y LeCun","year":"1995","unstructured":"LeCun Y, Bengio Y et al (1995) Convolutional networks for images, speech, and time series. Handb Brain Theory Neural Netw 3361(10):1995","journal-title":"Handb Brain Theory Neural Netw"},{"key":"5893_CR29","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"5893_CR30","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2212778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"5893_CR31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MSP.2015.2406955","volume":"32","author":"N Van Noord","year":"2015","unstructured":"Van Noord N, Hendriks E, Postma E (2015) Toward discovery of the artist\u2019s style: learning to recognize artists by their artworks. IEEE Signal Process Mag 32(4):46\u221254","journal-title":"IEEE Signal Process Mag"},{"key":"5893_CR32","unstructured":"Strezoski G, Worring M (2017) OmniArt: multi-task deep learning for artistic data analysis. arXiv preprint arXiv:1708.00684"},{"key":"5893_CR33","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks?. In: Advances in neural information processing systems, pp 3320\u22123328"},{"issue":"9","key":"5893_CR34","doi-asserted-by":"crossref","first-page":"11941","DOI":"10.1007\/s11042-016-4240-2","volume":"76","author":"M Budnik","year":"2017","unstructured":"Budnik M, Gutierrez-Gomez E-L, Safadi B, Pellerin D, Qu\u00e9not G (2017) Learned features versus engineered features for multimedia indexing. Multimed Tools Appl 76(9):11941\u221211958","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"5893_CR35","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s13735-019-00189-4","volume":"9","author":"N Garcia","year":"2020","unstructured":"Garcia N, Renoust B, Nakashima Y (2020) ContextNet: representation and exploration for painting classification and retrieval in context. Int J Multimed Inf Retrieval 9(1):17\u221230","journal-title":"Int J Multimed Inf Retrieval"},{"key":"5893_CR36","doi-asserted-by":"crossref","unstructured":"Castellano G, Vessio G (2020) Deep convolutional embedding for painting clustering: case study on Picasso\u2019s artworks. In: International conference on discovery science. Springer, pp 68\u221278","DOI":"10.1007\/978-3-030-61527-7_5"},{"issue":"2","key":"5893_CR37","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2020","unstructured":"Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietik\u00e4inen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261\u2212318","journal-title":"Int J Comput Vis"},{"key":"5893_CR38","unstructured":"Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: a survey. arXiv preprint arXiv:1905.05055"},{"key":"5893_CR39","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 580\u2212587","DOI":"10.1109\/CVPR.2014.81"},{"key":"5893_CR40","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"5893_CR41","doi-asserted-by":"crossref","unstructured":"Crowley EJ, Zisserman A (2014) In search of art. In: European Conference on Computer Vision. Springer, pp 54\u221270","DOI":"10.1007\/978-3-319-16178-5_4"},{"key":"5893_CR42","unstructured":"Cai H, Wu Q, Corradi T, Hall P (2015) The cross-depiction problem: computer vision algorithms for recognising objects in artwork and in photographs. arXiv preprint arXiv:1505.00110"},{"key":"5893_CR43","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, and Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672\u22122680"},{"key":"5893_CR44","doi-asserted-by":"crossref","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","volume":"7","author":"Z Pan","year":"2019","unstructured":"Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y (2019) Recent progress on generative adversarial networks (GANs): a survey. IEEE Access 7:36322\u221236333","journal-title":"IEEE Access"},{"key":"5893_CR45","unstructured":"Elgammal A, Liu B, Elhoseiny M, Mazzone M (2017) CAN: creative adversarial networks, generating \u201cart\u201d by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068"},{"key":"5893_CR46","unstructured":"Wiatrak M, Albrecht SV (2019) Stabilizing generative adversarial network training: a survey. arXiv preprint arXiv:1910.00927"},{"issue":"1","key":"5893_CR47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2200\/S00762ED1V01Y201703HLT037","volume":"10","author":"Y Goldberg","year":"2017","unstructured":"Goldberg Y (2017) Neural network methods for natural language processing. Synth Lect Hum Lang Technol 10(1):1\u2212309","journal-title":"Synth Lect Hum Lang Technol"},{"key":"5893_CR48","doi-asserted-by":"crossref","unstructured":"Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 6645\u22126649","DOI":"10.1109\/ICASSP.2013.6638947"},{"issue":"7","key":"5893_CR49","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235\u22121270","journal-title":"Neural Comput"},{"issue":"8","key":"5893_CR50","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","volume":"79","author":"JJ Hopfield","year":"1982","unstructured":"Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554\u22122558","journal-title":"Proc Natl Acad Sci"},{"key":"5893_CR51","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"5893_CR52","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1903.026780"},{"key":"5893_CR53","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u22121543","DOI":"10.3115\/v1\/D14-1162"},{"key":"5893_CR54","doi-asserted-by":"crossref","unstructured":"Garcia N, Ye C, Liu Z, Hu Q, Otani M, Chu C, Nakashima Y, Mitamura T (2020) A dataset and baselines for visual question answering on art. In: European conference on computer vision. Springer, pp 92\u2013108","DOI":"10.1007\/978-3-030-66096-3_8"},{"key":"5893_CR55","doi-asserted-by":"crossref","unstructured":"Cetinic E (2021) Iconographic image captioning for artworks. In: Del Bimbo A et al (eds) Pattern recognition. ICPR international workshops and challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12663. Springer, Cham","DOI":"10.1007\/978-3-030-68796-0_36"},{"key":"5893_CR56","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.patrec.2019.12.016","volume":"131","author":"F Ragusa","year":"2020","unstructured":"Ragusa F, Furnari A, Battiato S, Signorello G, Farinella GM (2020) EGO-CH: dataset and fundamental tasks for visitors behavioral understanding using egocentric vision. Pattern Recognit Lett 131:150\u2212157","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"5893_CR57","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10055-018-0366-z","volume":"24","author":"M Torres-Ruiz","year":"2020","unstructured":"Torres-Ruiz M, Mata F, Zagal R, Guzm\u00e1n G, Quintero R, Moreno-Ibarra M (2020) A recommender system to generate museum itineraries applying augmented reality and social-sensor mining techniques. Virtual Reality 24(1):175\u2212189","journal-title":"Virtual Reality"},{"key":"5893_CR58","doi-asserted-by":"crossref","unstructured":"Bar Y, Levy N, Wolf L (2014) Classification of artistic styles using binarized features derived from a deep neural network. In: European Conference on Computer Vision. Springer, pp 71\u221284","DOI":"10.1007\/978-3-319-16178-5_5"},{"key":"5893_CR59","doi-asserted-by":"crossref","unstructured":"Karayev S, Trentacoste M, Han H, Agarwala A, Darrell T, Hertzmann A, Winnemoeller H (2013) Recognizing image style. arXiv preprint arXiv:1903.026783","DOI":"10.5244\/C.28.122"},{"issue":"2","key":"5893_CR60","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The PASCAL visual object classes (VOC) challenge. Int J Comput Vis 88(2):303\u2212338","journal-title":"Int J Comput Vis"},{"key":"5893_CR61","unstructured":"Saleh B, Elgammal A (2015) Large-scale classification of fine-art paintings: learning the right metric on the right feature. arXiv preprint arXiv:1505.00855"},{"key":"5893_CR62","doi-asserted-by":"crossref","unstructured":"Tan WR, Chan CS, Aguirre HE, Tanaka K (2016) Ceci n\u2019est pas une pipe: a deep convolutional network for fine-art paintings classification. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE, pp 3703\u22123707","DOI":"10.1109\/ICIP.2016.7533051"},{"key":"5893_CR63","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.eswa.2018.07.026","volume":"114","author":"E Cetinic","year":"2018","unstructured":"Cetinic E, Lipic T, Grgic S (2018) Fine-tuning convolutional neural networks for fine art classification. Expert Syst Appl 114:107\u2212118","journal-title":"Expert Syst Appl"},{"key":"5893_CR64","doi-asserted-by":"crossref","unstructured":"Gonthier N, Gousseau Y, Ladjal S (2021) An analysis of the transfer learning of convolutional neural networks for artistic images. In: Del Bimbo A et al. (eds) Pattern recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12663. Springer, Cham","DOI":"10.1007\/978-3-030-68796-0_39"},{"key":"5893_CR65","doi-asserted-by":"crossref","unstructured":"Chen L, Yang J (2019) Recognizing the style of visual arts via adaptive cross-layer correlation. In: Proceedings of the 27th ACM international conference on multimedia, pp 2459\u22122467","DOI":"10.1145\/3343031.3350977"},{"key":"5893_CR66","doi-asserted-by":"crossref","first-page":"41770","DOI":"10.1109\/ACCESS.2019.2907986","volume":"7","author":"C Sandoval","year":"2019","unstructured":"Sandoval C, Pirogova E, Lech M (2019) Two-stage deep learning approach to the classification of fine-art paintings. IEEE Access 7:41770\u221241781","journal-title":"IEEE Access"},{"issue":"10","key":"5893_CR67","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.3390\/app8101768","volume":"8","author":"A Belhi","year":"2018","unstructured":"Belhi A, Bouras A, Foufou S (2018) Leveraging known data for missing label prediction in cultural heritage context. Appl Sci 8(10):1768","journal-title":"Appl Sci"},{"key":"5893_CR68","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855\u2212864","DOI":"10.1145\/2939672.2939754"},{"issue":"7","key":"5893_CR69","doi-asserted-by":"crossref","first-page":"3565","DOI":"10.1007\/s11042-014-2193-x","volume":"75","author":"B Saleh","year":"2016","unstructured":"Saleh B, Abe K, Arora RS, Elgammal A (2016) Toward automated discovery of artistic influence. Multimed Tools Appl 75(7):3565\u22123591","journal-title":"Multimed Tools Appl"},{"key":"5893_CR70","doi-asserted-by":"crossref","unstructured":"Seguin B, Striolo C, Kaplan F et al (2016) Visual link retrieval in a database of paintings. In: European Conference on Computer Vision. Springer, pp 753\u2212767","DOI":"10.1007\/978-3-319-46604-0_52"},{"key":"5893_CR71","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.culher.2017.11.008","volume":"31","author":"E Gultepe","year":"2018","unstructured":"Gultepe E, Conturo TE, Makrehchi M (2018) Predicting and grouping digitized paintings by style using unsupervised feature learning. J Cultural Heritage 31:13\u221223","journal-title":"J Cultural Heritage"},{"key":"5893_CR72","doi-asserted-by":"crossref","unstructured":"Castellano G, Vessio G (2020) Towards a tool for visual link retrieval and knowledge discovery in painting datasets. In: Italian Research Conference on Digital Libraries. Springer, pp 105\u2212110","DOI":"10.1007\/978-3-030-39905-4_11"},{"key":"5893_CR73","doi-asserted-by":"crossref","first-page":"6599","DOI":"10.1007\/s11042-020-09995-z","volume":"80","author":"G Castellano","year":"2020","unstructured":"Castellano G, Lella E, Vessio G (2020) Visual link retrieval and knowledge discovery in painting datasets. Multimed Tools Appl 80:6599\u22126616","journal-title":"Multimed Tools Appl"},{"key":"5893_CR74","doi-asserted-by":"crossref","unstructured":"Castellano G, Vessio G (2020) Deep convolutional embedding for digitized painting clustering. In: International Conference on Pattern Recognition (ICPR2020). IEEE","DOI":"10.1109\/ICPR48806.2021.9412438"},{"key":"5893_CR75","doi-asserted-by":"crossref","unstructured":"Baraldi L, Cornia M, Grana C, Cucchiara R (2018) Aligning text and document illustrations: towards visually explainable digital humanities. In: 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, pp 1097\u22121102","DOI":"10.1109\/ICPR.2018.8545064"},{"key":"5893_CR76","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.patrec.2019.11.018","volume":"129","author":"M Cornia","year":"2020","unstructured":"Cornia M, Stefanini M, Baraldi L, Corsini M, Cucchiara R (2020) Explaining digital humanities by aligning images and textual descriptions. Pattern Recognit Lett 129:166\u2212172","journal-title":"Pattern Recognit Lett"},{"key":"5893_CR77","doi-asserted-by":"crossref","unstructured":"Plummer BA, Wang L, Cervantes CM, Caicedo JC, Hockenmaier J, Lazebnik S (2015) Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. In: Proceedings of the IEEE international conference on computer vision, pp 2641\u22122649","DOI":"10.1109\/ICCV.2015.303"},{"key":"5893_CR78","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r, Zitnick (2014) Microsoft COCO: common objects in context. In: European conference on computer vision. Springer, pp 740\u2212755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"5893_CR79","doi-asserted-by":"crossref","unstructured":"Cai H, Wu Q, Hall P (2015) Beyond photo-domain object recognition: benchmarks for the cross-depiction problem. In: Proceedings of the IEEE international conference on computer vision workshops, pp 1\u22126","DOI":"10.1109\/ICCVW.2015.19"},{"key":"5893_CR80","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten LVD, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579\u22122605","journal-title":"J Mach Learn Res"},{"key":"5893_CR81","doi-asserted-by":"crossref","unstructured":"Crowley EJ, Zisserman A (2016) The art of detection. In: European conference on computer vision. Springer, pp 721\u2212737","DOI":"10.1007\/978-3-319-46604-0_50"},{"key":"5893_CR82","doi-asserted-by":"crossref","unstructured":"Gonthier N, Gousseau Y, Ladjal S, Bonfait O (2018) Weakly supervised object detection in artworks. In: Proceedings of the European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-030-11012-3_53"},{"key":"5893_CR83","doi-asserted-by":"crossref","unstructured":"Ufer N, Lang S, Ommer B (2020) Object retrieval and localization in large art collections using deep multi-style feature fusion and iterative voting. In: European conference on computer vision. Springer, pp 159\u2013176","DOI":"10.1007\/978-3-030-66096-3_12"},{"key":"5893_CR84","unstructured":"Pease A, Colton S (2011) On impact and evaluation in computational creativity: a discussion of the Turing test and an alternative proposal. In: Proceedings of the AISB symposium on AI and Philosophy, vol 39"},{"key":"5893_CR85","doi-asserted-by":"crossref","unstructured":"Tan WR, Chan CS, Aguirre HE, Tanaka K (2017) ArtGAN: artwork synthesis with conditional categorical GANs. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE, pp 3760\u2212764","DOI":"10.1109\/ICIP.2017.8296985"},{"issue":"1","key":"5893_CR86","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TIP.2018.2866698","volume":"28","author":"WR Tan","year":"2018","unstructured":"Tan WR, Chan CS, Aguirre HE, Tanaka K (2018) Improved ArtGAN for conditional synthesis of natural image and artwork. IEEE Trans Image Process 28(1):394\u2212409","journal-title":"IEEE Trans Image Process"},{"key":"5893_CR87","doi-asserted-by":"crossref","unstructured":"Lin M, Deng Y, Tang F, Dong W, Xu C (2020) Multi-attribute guided painting generation. In: 2020 IEEE conference on multimedia information processing and retrieval (MIPR). IEEE, pp 400\u2013403","DOI":"10.1109\/MIPR49039.2020.00088"},{"key":"5893_CR88","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 4401\u22124410","DOI":"10.1109\/CVPR.2019.00453"},{"key":"5893_CR89","unstructured":"Jin Y, Zhang J, Li M, Tian Y, Zhu H, Fang Z (2017) Towards the automatic anime characters creation with generative adversarial networks arXiv preprint arXiv:1903.026786"},{"issue":"9","key":"5893_CR90","doi-asserted-by":"crossref","first-page":"3540","DOI":"10.1109\/TNNLS.2019.2944979","volume":"31","author":"L Liu","year":"2019","unstructured":"Liu L, Zhang H, Xu X, Zhang Z, Yan S (2019) Collocating clothes with generative adversarial networks cosupervised by categories and attributes: a multidiscriminator framework. IEEE Trans Neural Netw Learn Syst 31(9):3540\u22123554","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5893_CR91","doi-asserted-by":"crossref","unstructured":"Tomei M, Cornia M, Baraldi L, Cucchiara R (2019) Art2Real: unfolding the reality of artworks via semantically-aware image-to-image translation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5849\u22125859","DOI":"10.1109\/CVPR.2019.00600"},{"key":"5893_CR92","doi-asserted-by":"crossref","unstructured":"Tomei M, Cornia M, Baraldi L, Cucchiara R (2019) Image-to-image translation to unfold the reality of artworks: an empirical analysis. In: International conference on image analysis and processing. Springer, pp 741\u2212752","DOI":"10.1007\/978-3-030-30645-8_67"},{"key":"5893_CR93","doi-asserted-by":"crossref","unstructured":"Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414\u22122423","DOI":"10.1109\/CVPR.2016.265"},{"key":"5893_CR94","first-page":"3365","volume":"36","author":"Y Jing","year":"2019","unstructured":"Jing Y, Yang Y, Feng Z, Ye J, Yu Y, Song M (2019) Neural style transfer: a review. IEEE Trans Vis Comput Graph 36:3365\u22123385","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"5893_CR95","doi-asserted-by":"crossref","unstructured":"Elgammal A, Kang Y, Den\u00a0Leeuw M (2018) Picasso, Matisse, or a fake? Automated analysis of drawings at the stroke level for attribution and authentication. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI Press, pp 42\u221250","DOI":"10.1609\/aaai.v32i1.11313"},{"key":"5893_CR96","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3016887","author":"Y Deng","year":"2020","unstructured":"Deng Y, Tang F, Dong W, Ma C, Huang F, Deussen O, Xu C (2020) Exploring the representativity of art paintings. IEEE Trans Multimed. https:\/\/doi.org\/10.1109\/TMM.2020.3016887","journal-title":"IEEE Trans Multimed"},{"key":"5893_CR97","doi-asserted-by":"crossref","unstructured":"Lu X, Sawant N, Newman MG, Adams RB, Wang JZ, Li J (2016) Identifying emotions aroused from paintings. In: European conference on computer vision. Springer, pp 48\u221263","DOI":"10.1007\/978-3-319-46604-0_4"},{"key":"5893_CR98","doi-asserted-by":"crossref","unstructured":"Cetinic E, Lipic T, Grgic S (2018) How convolutional neural networks remember art. In: 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, pp 1\u22125","DOI":"10.1109\/IWSSIP.2018.8439497"},{"key":"5893_CR99","doi-asserted-by":"crossref","first-page":"73694","DOI":"10.1109\/ACCESS.2019.2921101","volume":"7","author":"E Cetinic","year":"2019","unstructured":"Cetinic E, Lipic T, Grgic S (2019) A deep learning perspective on beauty, sentiment, and remembrance of art. IEEE Access 7:73694\u221273710","journal-title":"IEEE Access"},{"key":"5893_CR100","doi-asserted-by":"crossref","unstructured":"Isola P, Xiao J, Torralba A, Oliva A (2011) What makes an image memorable?. In: CVPR 2011. IEEE, pp 145\u2212152","DOI":"10.1109\/CVPR.2011.5995721"},{"key":"5893_CR101","doi-asserted-by":"crossref","unstructured":"Li D, Yang Y, Song Y-Z, Hospedales T (2018) Learning to generalize: Meta-learning for domain generalization. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"5893_CR102","doi-asserted-by":"crossref","unstructured":"Carlucci FM, D\u2019Innocente A, Bucci S, Caputo B, Tommasi T (2019) Domain generalization by solving jigsaw puzzles. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2229\u22122238","DOI":"10.1109\/CVPR.2019.00233"},{"key":"5893_CR103","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2212626","DOI":"10.1109\/ICCV.2017.74"},{"key":"5893_CR104","unstructured":"Jetley S, Lord NA, Lee N, Torr PH (2018) Learn to pay attention. arXiv preprint arXiv:1804.02391"},{"issue":"1","key":"5893_CR105","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1093\/jigpal\/jzz029","volume":"29","author":"V Costa","year":"2021","unstructured":"Costa V, Dellunde P, Falomir Z (2021) The logical style painting classifier based on Horn clauses and explanations (l-SHE). Log J IGPL 29(1):96\u2212119","journal-title":"Log J IGPL"},{"key":"5893_CR106","unstructured":"Aggarwal G, Parikh D (2020) Neuro-symbolic generative art: a preliminary study. arXiv preprint arXiv:2007.02171"},{"key":"5893_CR107","unstructured":"Amizadeh S, Palangi H, Polozov O, Huang Y, Koishida K (2020) Neuro-symbolic visual reasoning: disentangling visual from reasoning. arXiv preprint arXiv:2006.11524"},{"key":"5893_CR108","first-page":"141","volume":"3","author":"G Mercuriali","year":"2019","unstructured":"Mercuriali G (2019) Digital art history and the computational imagination. Int J Digit Art Hist Issue 3 2018 Digit Space Architect 3:141","journal-title":"Int J Digit Art Hist Issue 3 2018 Digit Space Architect"},{"issue":"1","key":"5893_CR109","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3233\/AIS-170467","volume":"10","author":"K Trejo","year":"2018","unstructured":"Trejo K, Angulo C, Satoh S, Bono M (2018) Towards robots reasoning about group behavior of museum visitors: leader detection and group tracking. J Ambient Intell Smart Environ 10(1):3\u221219","journal-title":"J Ambient Intell Smart Environ"},{"key":"5893_CR110","unstructured":"Castellano G, Carolis BD, Macchiarulo N, Vessio G (2020) Pepper4Museum: towards a human-like museum guide. In: Antoniou A, et al (eds) Proceedings of the AVI2CH workshop on advanced visual interfaces and interactions in cultural heritage, co-located with 2020 International Conference on Advanced Visual Interfaces (AVI 2020), vol 2687, CEUR-WS, 28 September\u22122 October 2020"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05893-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05893-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05893-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T07:54:51Z","timestamp":1671782091000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05893-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,2]]},"references-count":110,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["5893"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05893-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,2]]},"assertion":[{"value":"7 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}