{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T23:42:33Z","timestamp":1793144553499,"version":"4.5.0"},"reference-count":309,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100008425","name":"Conseller\u00eda de Cultura, Educaci\u00f3n e Ordenaci\u00f3n Universitaria, Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431G\/01"],"award-info":[{"award-number":["ED431G\/01"]}],"id":[{"id":"10.13039\/501100008425","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008425","name":"Conseller\u00eda de Cultura, Educaci\u00f3n e Ordenaci\u00f3n Universitaria, Xunta de Galicia","doi-asserted-by":"publisher","award":["ED431D 2017\/16"],"award-info":[{"award-number":["ED431D 2017\/16"]}],"id":[{"id":"10.13039\/501100008425","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008425","name":"Conseller\u00eda de Cultura, Educaci\u00f3n e Ordenaci\u00f3n Universitaria, Xunta de Galicia","doi-asserted-by":"publisher","award":["Ref. ED431C 2018\/49"],"award-info":[{"award-number":["Ref. ED431C 2018\/49"]}],"id":[{"id":"10.13039\/501100008425","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s00521-020-05565-4","type":"journal-article","created":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T21:06:49Z","timestamp":1610485609000},"page":"121-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":112,"title":["Artificial Neural Networks and Deep Learning in the Visual Arts: a review"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4910-3890","authenticated-orcid":false,"given":"Iria","family":"Santos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luz","family":"Castro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nereida","family":"Rodriguez-Fernandez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1lvaro","family":"Torrente-Pati\u00f1o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adri\u00e1n","family":"Carballal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,12]]},"reference":[{"issue":"4","key":"5565_CR1","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\u2013133","journal-title":"Bull Math Biophys"},{"key":"5565_CR2","unstructured":"Wani IM, Arora S (2020) Deep neural networks for diagnosis of osteoporosis: a review. In: Proceedings of ICRIC 2019. Springer, pp 65\u201378"},{"key":"5565_CR3","doi-asserted-by":"publisher","first-page":"2349","DOI":"10.1007\/s00345-019-03059-0","volume":"38","author":"M Negassi","year":"2020","unstructured":"Negassi M, Suarez-Ibarrola R, Hein S, Miernik A, Reiterer A (2020) Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects. World J Urol 38:2349\u20132358. https:\/\/doi.org\/10.1007\/s00345-019-03059-0","journal-title":"World J Urol"},{"key":"5565_CR4","doi-asserted-by":"crossref","unstructured":"Moon S, Ahmadnezhad P, Song H-J, Thompson J, Kipp K, Akinwuntan AE, Devos H (2020) Artificial neural networks in neurorehabilitation: a scoping review. NeuroRehabilitation 1\u201311","DOI":"10.3233\/NRE-192996"},{"key":"5565_CR5","doi-asserted-by":"crossref","unstructured":"Yucel M, Nigdeli SM, Bekda\u015f G (2020) Artificial neural networks (anns) and solution of civil engineering problems: anns and prediction applications. In: Artificial intelligence and machine learning applications in civil, mechanical, and industrial engineering. IGI Global, pp 13\u201337","DOI":"10.4018\/978-1-7998-0301-0.ch002"},{"key":"5565_CR6","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/978-3-030-10374-3_8","volume-title":"Laser scanning systems in highway and safety assessment","author":"B Pradhan","year":"2020","unstructured":"Pradhan B, Sameen MI (2020) Review of traffic accident predictions with neural networks. In: Laser scanning systems in highway and safety assessment. Springer, Cham, pp 97\u2013109"},{"issue":"4","key":"5565_CR7","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S1364-0321(01)00006-5","volume":"5","author":"SA Kalogirou","year":"2001","unstructured":"Kalogirou SA (2001) Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 5(4):373\u2013401","journal-title":"Renew Sustain Energy Rev"},{"issue":"12","key":"5565_CR8","first-page":"552","volume":"15","author":"A Sundararaj","year":"1999","unstructured":"Sundararaj A, Ravi R, Thirumalai P, Radhakrishnan G (1999) Artificial neural network applications in electrochemistry\u2014a review. Bull Electrochem 15(12):552\u2013555","journal-title":"Bull Electrochem"},{"issue":"1","key":"5565_CR9","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/TCIAIG.2015.2494596","volume":"9","author":"S Risi","year":"2015","unstructured":"Risi S, Togelius J (2015) Neuroevolution in games: state of the art and open challenges. IEEE Trans Comput Intell AI Games 9(1):25\u201341","journal-title":"IEEE Trans Comput Intell AI Games"},{"key":"5565_CR10","unstructured":"Lipton ZC, Berkowitz J, Elkan C A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019"},{"key":"5565_CR11","volume-title":"Artificial neural networks for speech and vision","author":"RJ Mammone","year":"1994","unstructured":"Mammone RJ (1994) Artificial neural networks for speech and vision, vol 4. Chapman & Hall, London"},{"issue":"1","key":"5565_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/neco.1989.1.1.1","volume":"1","author":"RP Lippmann","year":"1989","unstructured":"Lippmann RP (1989) Review of neural networks for speech recognition. Neural Comput 1(1):1\u201338","journal-title":"Neural Comput"},{"issue":"1","key":"5565_CR13","first-page":"61","volume":"3","author":"BC Kamble","year":"2016","unstructured":"Kamble BC (2016) Speech recognition using artificial neural network\u2014a review. Int J Comput Commun Instrum Eng 3(1):61\u201364","journal-title":"Int J Comput Commun Instrum Eng"},{"issue":"8","key":"5565_CR14","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/j.engappai.2004.08.011","volume":"17","author":"FM Dias","year":"2004","unstructured":"Dias FM, Antunes A, Mota AM (2004) Artificial neural networks: a review of commercial hardware. Eng Appl Artif Intell 17(8):945\u2013952","journal-title":"Eng Appl Artif Intell"},{"key":"5565_CR15","doi-asserted-by":"crossref","unstructured":"Giebel H (1971) Feature extraction and recognition of handwritten characters by homogeneous layers. In: Zeichenerkennung durch biologische und technische Systeme\/Pattern Recognition in Biological and Technical Systems. Springer, pp 162\u2013169","DOI":"10.1007\/978-3-642-65175-5_15"},{"issue":"4","key":"5565_CR16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193\u2013202","journal-title":"Biol Cybern"},{"issue":"4","key":"5565_CR17","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\u2013551","journal-title":"Neural Comput"},{"key":"5565_CR18","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"5565_CR19","unstructured":"Berg A, Deng J, Fei-Fei L (2010) Large scale visual recognition challenge (ilsvrc). http:\/\/www.image-net.org\/challenges\/LSVRC, p 3"},{"key":"5565_CR20","unstructured":"Le QV et al (2015) A tutorial on deep learning part 2: autoencoders, convolutional neural networks and recurrent neural networks. Google Brain 1\u201320"},{"key":"5565_CR21","first-page":"23","volume":"5","author":"J Wu","year":"2017","unstructured":"Wu J (2017) Introduction to convolutional neural networks, National Key Lab for Novel Software Technology. Nanjing Univ China 5:23","journal-title":"Nanjing Univ China"},{"issue":"1","key":"5565_CR22","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53\u201365","journal-title":"IEEE Signal Process Mag"},{"key":"5565_CR23","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-72877-1","volume-title":"The art of artificial evolution: a handbook on evolutionary art and music","author":"JJ Romero","year":"2008","unstructured":"Romero JJ (2008) The art of artificial evolution: a handbook on evolutionary art and music. Springer, Berlin"},{"key":"5565_CR24","doi-asserted-by":"crossref","unstructured":"Romero J (2020) Artificial intelligence in music, sound, art and design: 9th international conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15\u201317, 2020, Proceedings. Springer","DOI":"10.1007\/978-3-030-43859-3"},{"key":"5565_CR25","unstructured":"Association for computational creativity. https:\/\/computationalcreativity.net\/home\/"},{"key":"5565_CR26","unstructured":"The bridges conference. https:\/\/www.bridgesmathart.org\/"},{"key":"5565_CR27","unstructured":"Evomusart. https:\/\/evomusart-index.dei.uc.pt\/"},{"issue":"3","key":"5565_CR28","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1162\/ARTL_e_00166","volume":"21","author":"A Dorin","year":"2015","unstructured":"Dorin A (2015) Artificial life art, creativity, and techno-hybridization (editor\u2019s introduction). Artif Life 21(3):261\u2013270","journal-title":"Artif Life"},{"issue":"2\u20133","key":"5565_CR29","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/17513472.2012.688185","volume":"6","author":"G Greenfield","year":"2012","unstructured":"Greenfield G, Machado P (2012) Guest editor\u2019s introduction, special issue on mathematical models used in aesthetic evaluation. J Math Arts 6(2\u20133):59\u201364","journal-title":"J Math Arts"},{"key":"5565_CR30","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1155\/2019\/9836102","volume":"2019","author":"J Romero","year":"2019","unstructured":"Romero J, Johnson C, McCormack J (2019) Complex systems in aesthetics and arts. Complexity 2019:2. https:\/\/doi.org\/10.1155\/2019\/9836102","journal-title":"Complexity"},{"key":"5565_CR31","doi-asserted-by":"crossref","unstructured":"Galanter P (2012) Computational aesthetic evaluation: steps towards machine creativity. In: ACM SIGGRAPH 2012 courses. pp 1\u2013162","DOI":"10.1145\/2343483.2343497"},{"key":"5565_CR32","unstructured":"Spratt EL, Elgammal A (2014) Computational beauty: aesthetic judgment at the intersection of art and science. In: European conference on computer vision. Springer, pp 35\u201353"},{"issue":"6","key":"5565_CR33","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1002\/widm.1170","volume":"5","author":"H Toivonen","year":"2015","unstructured":"Toivonen H, Gross O (2015) Data mining and machine learning in computational creativity. Data Min Knowl Disc 5(6):265\u2013275","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"5565_CR34","first-page":"433","volume":"9","author":"N Upadhyaya","year":"2016","unstructured":"Upadhyaya N, Dixit M, Pradesh DM (2016) A review: relating low level features to high level semantics in cbir. Int J Signal Process Image Process Pattern Recognit 9(3):433\u2013444","journal-title":"Int J Signal Process Image Process Pattern Recognit"},{"key":"5565_CR35","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1155\/2019\/3495962","volume":"2019","author":"CG Johnson","year":"2019","unstructured":"Johnson CG, McCormack J, Santos I, Romero J (2019) Understanding aesthetics and fitness measures in evolutionary art systems. Complexity 2019:14. https:\/\/doi.org\/10.1155\/2019\/3495962","journal-title":"Complexity"},{"key":"5565_CR36","unstructured":"Todd PM, Werner GM (1999) Frankensteinian methods for evolutionary music. Musical networks: parallel distributed perception and performance, pp 313\u2013340"},{"key":"5565_CR37","doi-asserted-by":"crossref","unstructured":"Lewis M (2008) Evolutionary visual art and design. In: The art of artificial evolution. Springer, Berlin, pp 3\u201337","DOI":"10.1007\/978-3-540-72877-1_1"},{"key":"5565_CR38","unstructured":"Briot J-P, Hadjeres G, Pachet F-D, Deep learning techniques for music generation\u2014a survey. arXiv preprint arXiv:1709.01620"},{"key":"5565_CR39","volume-title":"Deep learning techniques for music generation","author":"J-P Briot","year":"2019","unstructured":"Briot J-P, Hadjeres G, Pachet F (2019) Deep learning techniques for music generation, vol 10. Springer, Berlin"},{"key":"5565_CR40","unstructured":"Papers index of this soa. https:\/\/cutt.ly\/4fCCWGs"},{"key":"5565_CR41","unstructured":"Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: a deep convolutional activation feature for generic visual recognition. In: International conference on machine learning, pp 647\u2013655"},{"key":"5565_CR42","unstructured":"Elhoseiny M, Cohen S, Chang W, Price B, Elgammal A, Automatic annotation of structured facts in images. arXiv:1604.00466"},{"key":"5565_CR43","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\u20131211","DOI":"10.1109\/ICCV.2017.136"},{"key":"5565_CR44","doi-asserted-by":"crossref","unstructured":"Masui K, Ochiai A, Yoshizawa S, Nakayama H (2017) Recurrent visual relationship recognition with triplet unit. In: 2017 IEEE international symposium on multimedia (ISM). IEEE, pp 69\u201376","DOI":"10.1109\/ISM.2017.20"},{"key":"5565_CR45","unstructured":"Nguyen K. Relational networks for visual relationship detection in images"},{"key":"5565_CR46","unstructured":"Zhang J, Shih K, Tao A, Catanzaro B, Elgammal A. Introduction to the 1st place winning model of openimages relationship detection challenge. arXiv:1811.00662"},{"key":"5565_CR47","unstructured":"Peyre J, Laptev I, Schmid C, Sivic J, Detecting rare visual relations using analogies. arXiv:1812.05736"},{"key":"5565_CR48","unstructured":"Detecci\u00f3n del logotipo del veh\u00edculo utilizando una red neuronal convolucional y una pir\u00e1mide de histograma de gradientes orientados, in: $$11^{{\\rm a}}$$ Conferencia Internacional Conjunta de 2014 sobre Ciencias de la Computaci\u00f3n e Ingenier\u00eda de Software (JCSSE)"},{"key":"5565_CR49","doi-asserted-by":"publisher","unstructured":"Dulecha TG, Giachetti A, Pintus R, Ciortan I, Jaspe A, Gobbetti E (2019) Crack detection in single- and multi-light images of painted surfaces using convolutional neural networks. In: Eurographics Workshop on Graphics and Cultural Heritage. https:\/\/doi.org\/10.2312\/gch.20191347","DOI":"10.2312\/gch.20191347"},{"issue":"2","key":"5565_CR50","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s41095-015-0017-1","volume":"1","author":"P Hall","year":"2015","unstructured":"Hall P, Cai H, Wu Q, Corradi T (2015) Cross-depiction problem: recognition and synthesis of photographs and artwork. Comput Visual Media 1(2):91\u2013103","journal-title":"Comput Visual Media"},{"key":"5565_CR51","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, Berlin, pp 753\u2013767","DOI":"10.1007\/978-3-319-46604-0_52"},{"key":"5565_CR52","doi-asserted-by":"crossref","unstructured":"Inoue N, Furuta R, Yamasaki T, Aizawa K (2018) Cross-domain weakly-supervised object detection through progressive domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5001\u20135009","DOI":"10.1109\/CVPR.2018.00525"},{"key":"5565_CR53","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":"5565_CR54","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp 91\u201399"},{"key":"5565_CR55","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\u2013841","DOI":"10.1007\/978-3-319-46604-0_57"},{"key":"5565_CR56","doi-asserted-by":"crossref","unstructured":"Seguin B, Costiner L, di Lenardo I, Kaplan F (2018) New techniques for the digitization of art historical photographic archives-the case of the cini foundation in venice. In: Archiving conference, vol 2018. Society for Imaging Science and Technology, pp 1\u20135","DOI":"10.2352\/issn.2168-3204.2018.1.0.2"},{"key":"5565_CR57","unstructured":"Gonthier N, Ladjal S, Gousseau Y. Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts. arXiv:2008.01178"},{"key":"5565_CR58","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, Berlin, pp 740\u2013755"},{"issue":"6","key":"5565_CR59","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","journal-title":"Psychol Rev"},{"key":"5565_CR60","unstructured":"Thomas C, Kovashka A (2018) Artistic object recognition by unsupervised style adaptation. In: Asian conference on computer vision. Springer, pp 460\u2013476"},{"key":"5565_CR61","unstructured":"Crowley EJ, Zisserman A (2014) In search of art. In: European conference on computer vision. Springer, pp 54\u201370"},{"issue":"7","key":"5565_CR62","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/jimaging4070089","volume":"4","author":"N-V Nguyen","year":"2018","unstructured":"Nguyen N-V, Rigaud C, Burie J-C (2018) Digital comics image indexing based on deep learning. J Imaging 4(7):89","journal-title":"J Imaging"},{"key":"5565_CR63","unstructured":"Ogawa T, Otsubo A, Narita R, Matsui Y, Yamasaki T, Aizawa K. Object detection for comics using manga109 annotations. arXiv:1803.08670"},{"issue":"20","key":"5565_CR64","doi-asserted-by":"crossref","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","volume":"76","author":"Y Matsui","year":"2017","unstructured":"Matsui Y, Ito K, Aramaki Y, Fujimoto A, Ogawa T, Yamasaki T, Aizawa K (2017) Sketch-based manga retrieval using manga109 dataset. Multimed Tools Appl 76(20):21811\u201321838","journal-title":"Multimed Tools Appl"},{"key":"5565_CR65","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"5565_CR66","doi-asserted-by":"crossref","unstructured":"Niitani Y, Ogawa T, Saito S, Saito M (2017) Chainercv: a library for deep learning in computer vision. In: Proceedings of the 25th ACM international conference on multimedia. pp 1217\u20131220","DOI":"10.1145\/3123266.3129395"},{"key":"5565_CR67","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"5565_CR68","unstructured":"Simonyan K, Zisserman A, Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"5565_CR69","unstructured":"Dubray D, Laubrock J, Deep cnn-based speech balloon detection and segmentation for comic books. arXiv:1902.08137"},{"key":"5565_CR70","doi-asserted-by":"crossref","unstructured":"Dunst A, Hartel R, Laubrock J (2017) The graphic narrative corpus (gnc): design, annotation, and analysis for the digital humanities. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 3. IEEE, pp 15\u201320","DOI":"10.1109\/ICDAR.2017.286"},{"key":"5565_CR71","doi-asserted-by":"crossref","unstructured":"Murray N, Marchesotti L, Perronnin F (2012) Ava: a large-scale database for aesthetic visual analysis. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 2408\u20132415","DOI":"10.1109\/CVPR.2012.6247954"},{"issue":"11","key":"5565_CR72","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/TMM.2015.2477040","volume":"17","author":"X Lu","year":"2015","unstructured":"Lu X, Lin Z, Jin H, Yang J, Wang JZ (2015) Rating image aesthetics using deep learning. IEEE Trans Multimed 17(11):2021\u20132034","journal-title":"IEEE Trans Multimed"},{"key":"5565_CR73","unstructured":"Karayev S, Trentacoste M, Han H, Agarwala A, Darrell T, Hertzmann A, Winnemoeller H, Recognizing image style. arXiv:1311.3715"},{"key":"5565_CR74","unstructured":"Website: Flickr. https:\/\/www.flickr.com\/"},{"key":"5565_CR75","unstructured":"All data, trained predictors, and code of sergey karayev. http:\/\/sergeykarayev.com\/recognizing-image-style"},{"key":"5565_CR76","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia. pp 675\u2013678","DOI":"10.1145\/2647868.2654889"},{"key":"5565_CR77","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\u201384"},{"key":"5565_CR78","unstructured":"Wikiart. visual art encyclopedia. https:\/\/www.wikiart.org\/en\/artists-by-painting-school"},{"key":"5565_CR79","unstructured":"Bergamo A, Torresani L, Fitzgibbon AW (2011) Picodes: learning a compact code for novel-category recognition. In: Advances in neural information processing systems. pp 2088\u20132096"},{"issue":"6","key":"5565_CR80","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\u20131397","journal-title":"Mach Vis Appl"},{"key":"5565_CR81","doi-asserted-by":"crossref","unstructured":"Mensink T, Van\u00a0Gemert J (2014) The rijksmuseum challenge: museum-centered visual recognition. In: Proceedings of international conference on multimedia retrieval. ACM, p 451","DOI":"10.1145\/2578726.2578791"},{"issue":"4","key":"5565_CR82","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\u201354","journal-title":"IEEE Signal Process Mag"},{"key":"5565_CR83","doi-asserted-by":"crossref","unstructured":"Jboor NH, Belhi A, Al-Ali AK, Bouras A, Jaoua A (2019) Towards an inpainting framework for visual cultural heritage. In: 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT). IEEE, pp 602\u2013607","DOI":"10.1109\/JEEIT.2019.8717470"},{"key":"5565_CR84","doi-asserted-by":"crossref","unstructured":"Castro L, Perez R, Santos A, Carballal A (2014) Authorship and aesthetics experiments: comparison of results between human and computational systems. In: International conference on evolutionary and biologically inspired music and art. Springer, pp 74\u201384","DOI":"10.1007\/978-3-662-44335-4_7"},{"issue":"1","key":"5565_CR85","doi-asserted-by":"crossref","first-page":"261","DOI":"10.2466\/pms.1974.39.1.261","volume":"39","author":"KO G\u00f6tz","year":"1974","unstructured":"G\u00f6tz KO, G\u00f6tz K (1974) The maitland graves design judgment test judged by 22 experts. Percept Mot Skills 39(1):261\u2013262","journal-title":"Percept Mot Skills"},{"issue":"4","key":"5565_CR86","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1037\/h0031469","volume":"55","author":"H Eysenck","year":"1971","unstructured":"Eysenck H, Castle M (1971) Comparative study of artists and nonartists on the maitland graves design judgment test. J Appl Psychol 55(4):389","journal-title":"J Appl Psychol"},{"key":"5565_CR87","doi-asserted-by":"crossref","unstructured":"Machado P, Romero J, Manaris B (2008) Experiments in computational aesthetics. In: The art of artificial evolution. Springer, pp 381\u2013415","DOI":"10.1007\/978-3-540-72877-1_18"},{"key":"5565_CR88","doi-asserted-by":"crossref","unstructured":"Saleh B, Elgammal A (2015) A unified framework for painting classification. In: 2015 IEEE international conference on data mining workshop (ICDMW). IEEE, pp 1254\u20131261","DOI":"10.1109\/ICDMW.2015.93"},{"issue":"3","key":"5565_CR89","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","volume":"42","author":"A Oliva","year":"2001","unstructured":"Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145\u2013175","journal-title":"Int J Comput Vis"},{"key":"5565_CR90","doi-asserted-by":"crossref","unstructured":"Torresani L, Szummer M, Fitzgibbon A (2010) Efficient object category recognition using classemes. In: European conference on computer vision. Springer, pp 776\u2013789","DOI":"10.1007\/978-3-642-15549-9_56"},{"key":"5565_CR91","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\u20133707","DOI":"10.1109\/ICIP.2016.7533051"},{"key":"5565_CR92","unstructured":"Tan WR, Chan CS, Aguirre HE, Tanaka K, Ceci n\u2019est pas une pipe: a deep convolutional network for fine-art paintings classification"},{"key":"5565_CR93","unstructured":"Saleh B, Elgammal A, Large-scale classification of fine-art paintings: learning the right metric on the right feature. arXiv:1505.00855"},{"key":"5565_CR94","unstructured":"Banerji S, Sinha A (2016) Painting classification using a pre-trained convolutional neural network. In: International conference on computer vision, graphics, and image processing. Springer, pp 168\u2013179"},{"key":"5565_CR95","unstructured":"Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y, Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229"},{"key":"5565_CR96","unstructured":"Baumer M, Chen D, Understanding visual art with cnns"},{"key":"5565_CR97","doi-asserted-by":"crossref","unstructured":"Bianco S, Mazzini D, Schettini R (2017) Deep multibranch neural network for painting categorization. In: International conference on image analysis and processing. Springer, pp 414\u2013423","DOI":"10.1007\/978-3-319-68560-1_37"},{"key":"5565_CR98","unstructured":"Lecoutre A, Negrevergne B, Yger F, Recognizing art style automatically with deep learning"},{"issue":"1","key":"5565_CR99","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/TCIAIG.2017.2681042","volume":"10","author":"T Cazenave","year":"2017","unstructured":"Cazenave T (2017) Residual networks for computer go. IEEE Trans Games 10(1):107\u2013110","journal-title":"IEEE Trans Games"},{"key":"5565_CR100","unstructured":"ergart. https:\/\/www.ergsart.com"},{"key":"5565_CR101","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\u20131191","DOI":"10.1145\/3123266.3123405"},{"key":"5565_CR102","unstructured":"Deepart. http:\/\/deepart2.ece.ust.hk\/"},{"key":"5565_CR103","unstructured":"Art500k. http:\/\/deepart2.ece.ust.hk\/ART500K\/art500k.html"},{"key":"5565_CR104","unstructured":"Google arts and culture. https:\/\/artsandculture.google.com\/"},{"key":"5565_CR105","unstructured":"Web gallery of art. https:\/\/www.wga.hu\/"},{"key":"5565_CR106","unstructured":"Strezoski G, Worring M, Omniart: multi-task deep learning for artistic data analysis. arXiv:1708.00684"},{"key":"5565_CR107","unstructured":"Met. https:\/\/www.metmuseum.org\/art\/collection"},{"key":"5565_CR108","unstructured":"Omniart dataset. http:\/\/www.vistory-omniart.com\/"},{"issue":"2","key":"5565_CR109","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1017\/S0307472200003436","volume":"8","author":"LD Couprie","year":"1983","unstructured":"Couprie LD (1983) Iconclass: an iconographic classification system. Art Librar J 8(2):32\u201349","journal-title":"Art Librar J"},{"key":"5565_CR110","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5565_CR111","doi-asserted-by":"crossref","unstructured":"Hicsonmez S, Samet N, Sener F, Duygulu P (2017) Draw: deep networks for recognizing styles of artists who illustrate children\u2019s books. In: Proceedings of the 2017 ACM on international conference on multimedia retrieval. pp 338\u2013346","DOI":"10.1145\/3078971.3078982"},{"issue":"2","key":"5565_CR112","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91\u2013110","journal-title":"Int J Comput Vis"},{"key":"5565_CR113","doi-asserted-by":"crossref","unstructured":"Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol 2. IEEE, pp 1150\u20131157","DOI":"10.1109\/ICCV.1999.790410"},{"key":"5565_CR114","doi-asserted-by":"crossref","unstructured":"Sener F, Samet N, Sahin PD (2012) Identification of illustrators. In: European conference on computer vision. Springer, pp 589\u2013597","DOI":"10.1007\/978-3-642-33863-2_61"},{"key":"5565_CR115","doi-asserted-by":"crossref","unstructured":"Rodriguez CS, Lech M, Pirogova E (2018) Classification of style in fine-art paintings using transfer learning and weighted image patches. In: 2018 12th international conference on signal processing and communication systems (ICSPCS). IEEE, pp 1\u20137","DOI":"10.1109\/ICSPCS.2018.8631731"},{"key":"5565_CR116","unstructured":"Florea C, Condorovici R, Vertan C (2018) Pandora. http:\/\/imag.pub.ro\/pandora\/pandora_download.html"},{"key":"5565_CR117","doi-asserted-by":"crossref","unstructured":"Florea C, Toca C, Gieseke F (2017) Artistic movement recognition by boosted fusion of color structure and topographic description. In: 2017 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 569\u2013577","DOI":"10.1109\/WACV.2017.69"},{"issue":"3","key":"5565_CR118","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/C-M.1977.217671","volume":"10","author":"CN Mooers","year":"1977","unstructured":"Mooers CN (1977) Preventing software piracy. Computer 10(3):29\u201330","journal-title":"Computer"},{"key":"5565_CR119","doi-asserted-by":"publisher","first-page":"12635","DOI":"10.1007\/s11042-019-08547-4","volume":"79","author":"K-L Hua","year":"2020","unstructured":"Hua K-L, Ho T-T, Jangtjik K-A, Chen Y-J, Yeh M-C (2020) Artist-based painting classification using Markov random fields with convolution neural network. Multimed Tools Appl 79:12635\u201312658. https:\/\/doi.org\/10.1007\/s11042-019-08547-4","journal-title":"Multimed Tools Appl"},{"key":"5565_CR120","doi-asserted-by":"crossref","unstructured":"Jangtjik KA, Yeh M-C, Hua K-L (2016) Artist-based classification via deep learning with multi-scale weighted pooling. In: Proceedings of the 24th ACM international conference on Multimedia. pp 635\u2013639","DOI":"10.1145\/2964284.2967299"},{"key":"5565_CR121","doi-asserted-by":"crossref","unstructured":"Elgammal A, Kang Y, Den Leeuw M (2018) Picasso, matisse, or a fake? Automated analysis of drawings at the stroke level for attribution and authentication. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11313"},{"key":"5565_CR122","unstructured":"Chen J, Deng A (2018) Comparison of machine learning techniques for artist identification"},{"key":"5565_CR123","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\u201341781","journal-title":"IEEE Access"},{"key":"5565_CR124","unstructured":"Kim Y-M (2018) What makes the difference in visual styles of comics: from classification to style transfer. In: 2018 3rd international conference on computational intelligence and applications (ICCIA). IEEE, pp 181\u2013185"},{"issue":"1","key":"5565_CR125","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s40537-019-0222-3","volume":"6","author":"K Young-Min","year":"2019","unstructured":"Young-Min K (2019) Feature visualization in comic artist classification using deep neural networks. J Big Data 6(1):56","journal-title":"J Big Data"},{"key":"5565_CR126","doi-asserted-by":"crossref","unstructured":"Furusawa C, Hiroshiba K, Ogaki K, Odagiri Y (2017) Comicolorization: semi-automatic manga colorization. In: SIGGRAPH Asia 2017 Technical Briefs. pp 1\u20134","DOI":"10.1145\/3145749.3149430"},{"key":"5565_CR127","doi-asserted-by":"crossref","unstructured":"Yoshimura Y, Cai B, Wang Z, Ratti C (2019) Deep learning architect: classification for architectural design through the eye of artificial intelligence. In: International conference on computers in urban planning and urban management. Springer, pp 249\u2013265","DOI":"10.1007\/978-3-030-19424-6_14"},{"key":"5565_CR128","unstructured":"Liao W, Lan C, Zeng W, Yang MY, Rosenhahn B, Exploring the semantics for visual relationship detection. arXiv:1904.02104"},{"key":"5565_CR129","doi-asserted-by":"crossref","unstructured":"Zhang J, Shih KJ, Elgammal A, Tao A, Catanzaro B (2019) Graphical contrastive losses for scene graph parsing. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 11535\u201311543","DOI":"10.1109\/CVPR.2019.01180"},{"key":"5565_CR130","doi-asserted-by":"crossref","unstructured":"Gu J, Zhao H, Lin Z, Li S, Cai J, Ling M (2019) Scene graph generation with external knowledge and image reconstruction. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1969\u20131978","DOI":"10.1109\/CVPR.2019.00207"},{"key":"5565_CR131","doi-asserted-by":"crossref","unstructured":"Zhang J, Kalantidis Y, Rohrbach M, Paluri M, Elgammal A, Elhoseiny M (2019) Large-scale visual relationship understanding. In: Proceedings of the AAAI conference on artificial intelligence, vol 33. pp 9185\u20139194","DOI":"10.1609\/aaai.v33i01.33019185"},{"issue":"11","key":"5565_CR132","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/TMM.2015.2479916","volume":"17","author":"X Tian","year":"2015","unstructured":"Tian X, Dong Z, Yang K, Mei T (2015) Query-dependent aesthetic model with deep learning for photo quality assessment. IEEE Trans Multimed 17(11):2035\u20132048","journal-title":"IEEE Trans Multimed"},{"key":"5565_CR133","doi-asserted-by":"crossref","unstructured":"Luo W, Wang X, Tang X (2011) Content-based photo quality assessment. In: 2011 International conference on computer vision. IEEE, pp 2206\u20132213","DOI":"10.1109\/ICCV.2011.6126498"},{"key":"5565_CR134","unstructured":"Wagner M, Lin H, Li S, Saupe D, Algorithm selection for image quality assessment. arXiv:1908.06911"},{"key":"5565_CR135","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.actpsy.2015.06.005","volume":"160","author":"P Machado","year":"2015","unstructured":"Machado P, Romero J, Nadal M, Santos A, Correia J, Carballal A (2015) Computerized measures of visual complexity. Acta Psychol 160:43\u201357","journal-title":"Acta Psychol"},{"key":"5565_CR136","volume-title":"Neural networks: a comprehensive foundation","author":"S Haykin","year":"1994","unstructured":"Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall PTR, London"},{"key":"5565_CR137","doi-asserted-by":"crossref","unstructured":"Denzler J, Rodner E, Simon M (2016) Convolutional neural networks as a computational model for the underlying processes of aesthetics perception. In: European conference on computer vision. Springer, pp 871\u2013887","DOI":"10.1007\/978-3-319-46604-0_60"},{"key":"5565_CR138","unstructured":"Amirshahi SA, Denzler J, Redies C, Jenaesthetics\u2014a public dataset of paintings for aesthetic research. Computer Vision Group [Google Scholar], Jena"},{"key":"5565_CR139","doi-asserted-by":"crossref","unstructured":"Redies C, Amirshahi SA, Koch M, Denzler J (2012) Phog-derived aesthetic measures applied to color photographs of artworks, natural scenes and objects. In: European conference on computer vision. Springer, pp 522\u2013531","DOI":"10.1007\/978-3-642-33863-2_54"},{"key":"5565_CR140","doi-asserted-by":"crossref","unstructured":"Amirshahi SA, Redies C, Denzler J (2013) How self-similar are artworks at different levels of spatial resolution?. In: Proceedings of the symposium on computational aesthetics, pp 93\u2013100","DOI":"10.1145\/2487276.2487282"},{"issue":"6","key":"5565_CR141","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.1007\/s00521-016-2787-5","volume":"30","author":"A Carballal","year":"2018","unstructured":"Carballal A, Santos A, Romero J, Machado P, Correia J, Castro L (2018) Distinguishing paintings from photographs by complexity estimates. Neural Comput Appl 30(6):1957\u20131969","journal-title":"Neural Comput Appl"},{"issue":"11","key":"5565_CR142","first-page":"384","volume":"7","author":"M Prasad","year":"2018","unstructured":"Prasad M, Jwala Lakshmamma B, Chandana AH, Komali K, Manoja M, Rajesh Kumar P, Sasi Kiran P (2018) An efficient classification of flower images with convolutional neural networks. Int J Eng Technol 7(11):384\u2013391","journal-title":"Int J Eng Technol"},{"key":"5565_CR143","doi-asserted-by":"crossref","unstructured":"Collomosse J, Bui T, Wilber MJ, Fang C, Jin H (2017) Sketching with style: visual search with sketches and aesthetic context. In: Proceedings of the IEEE international conference on computer vision, pp 2660\u20132668","DOI":"10.1109\/ICCV.2017.290"},{"issue":"4","key":"5565_CR144","first-page":"1","volume":"31","author":"M Eitz","year":"2012","unstructured":"Eitz M, Hays J, Alexa M (2012) How do humans sketch objects? ACM Trans Graphics TOG 31(4):1\u201310","journal-title":"ACM Trans Graphics TOG"},{"key":"5565_CR145","unstructured":"Lu NGM, Deformsketchnet: Deformable convolutional networks for sketch classification"},{"key":"5565_CR146","unstructured":"Shen X, Efros AA, Mathieu A, Discovering visual patterns in art collections with spatially-consistent feature learning. arXiv:1903.02678"},{"key":"5565_CR147","unstructured":"Brueghel family: Jan brueghel the elder. University of california, Berkeley. https:\/\/www.janbrueghel.net\/"},{"key":"5565_CR148","unstructured":"Dutta A, Zisserman A, The vgg image annotator (via). arXiv:1904.10699"},{"issue":"1","key":"5565_CR149","doi-asserted-by":"crossref","first-page":"011010","DOI":"10.1117\/1.JEI.26.1.011010","volume":"26","author":"S En","year":"2016","unstructured":"En S, Nicolas S, Petitjean C, Jurie F, Heutte L (2016) New public dataset for spotting patterns in medieval document images. J Electron Imaging 26(1):011010","journal-title":"J Electron Imaging"},{"key":"5565_CR150","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.cviu.2015.05.016","volume":"139","author":"B Fernando","year":"2015","unstructured":"Fernando B, Tommasi T, Tuytelaars T (2015) Location recognition over large time lags. Comput Vis Image Underst 139:21\u201328","journal-title":"Comput Vis Image Underst"},{"key":"5565_CR151","doi-asserted-by":"crossref","unstructured":"Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In IEEE conference on computer vision and pattern recognition. IEEE, pp 1\u20138","DOI":"10.1109\/CVPR.2007.383172"},{"key":"5565_CR152","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\u2013110","DOI":"10.1007\/978-3-030-39905-4_11"},{"key":"5565_CR153","unstructured":"Kaggle. https:\/\/www.kaggle.com\/ikarus777\/best-artworks-of-all-time"},{"key":"5565_CR154","doi-asserted-by":"crossref","unstructured":"Datta R, Joshi D, Li J, Wang JZ (2006) Studying aesthetics in photographic images using a computational approach. In: European conference on computer vision. Springer, pp 288\u2013301","DOI":"10.1007\/11744078_23"},{"key":"5565_CR155","unstructured":"Ke Y, Tang X, Jing F (2006) The design of high-level features for photo quality assessment. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201906), vol\u00a01. IEEE, pp 419\u2013426"},{"key":"5565_CR156","unstructured":"Wong L-K, Low K-L (2009) Saliency-enhanced image aesthetics class prediction. In: 2009 16th IEEE international conference on image processing (ICIP). pp 997\u20131000"},{"key":"5565_CR157","doi-asserted-by":"crossref","unstructured":"Marchesotti L, Perronnin F, Larlus D, Csurka G (2011) Assessing the aesthetic quality of photographs using generic image descriptors. In: 2011 international conference on computer vision. IEEE, pp 1784\u20131791","DOI":"10.1109\/ICCV.2011.6126444"},{"key":"5565_CR158","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.neucom.2014.12.106","volume":"172","author":"W Wang","year":"2016","unstructured":"Wang W, Cai D, Wang L, Huang Q, Xu X, Li X (2016) Synthesized computational aesthetic evaluation of photos. Neurocomputing 172:244\u2013252","journal-title":"Neurocomputing"},{"issue":"8","key":"5565_CR159","doi-asserted-by":"crossref","first-page":"1811","DOI":"10.1109\/TMM.2017.2679900","volume":"19","author":"Y Xia","year":"2017","unstructured":"Xia Y, Liu Z, Yan Y, Chen Y, Zhang L, Zimmermann R (2017) Media quality assessment by perceptual gaze-shift patterns discovery. IEEE Trans Multimedia 19(8):1811\u20131820","journal-title":"IEEE Trans Multimedia"},{"key":"5565_CR160","doi-asserted-by":"crossref","unstructured":"Tong H, Li M, Zhang H-J, He J, Zhang C (2004) Classification of digital photos taken by photographers or home users. In: Pacific-Rim conference on multimedia. Springer, pp 198\u2013205","DOI":"10.1007\/978-3-540-30541-5_25"},{"issue":"2","key":"5565_CR161","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman J, Hastie T, Tibshirani R et al (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Statist 28(2):337\u2013407","journal-title":"Ann Statist"},{"key":"5565_CR162","doi-asserted-by":"crossref","unstructured":"Luo Y, Tang X (2008) Photo and video quality evaluation: focusing on the subject. In: European conference on computer vision. Springer, pp 386\u2013399","DOI":"10.1007\/978-3-540-88690-7_29"},{"key":"5565_CR163","doi-asserted-by":"crossref","unstructured":"Wu O, Hu W, Gao J (2011) Learning to predict the perceived visual quality of photos. In: 2011 international conference on computer vision. IEEE, pp 225\u2013232","DOI":"10.1109\/ICCV.2011.6126246"},{"key":"5565_CR164","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2015.04.124","volume":"188","author":"Y Tan","year":"2016","unstructured":"Tan Y, Zhou Y, Li G, Huang A (2016) Computational aesthetics of photos quality assessment based on improved artificial neural network combined with an autoencoder technique. Neurocomputing 188:50\u201362","journal-title":"Neurocomputing"},{"key":"5565_CR165","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.neucom.2017.01.054","volume":"257","author":"F Gao","year":"2017","unstructured":"Gao F, Wang Y, Li P, Tan M, Yu J, Zhu Y (2017) Deepsim: deep similarity for image quality assessment. Neurocomputing 257:104\u2013114","journal-title":"Neurocomputing"},{"key":"5565_CR166","doi-asserted-by":"crossref","unstructured":"Meng X, Gao F, Shi S, Zhu S, Zhu J (2018) Mlans: image aesthetic assessment via multi-layer aggregation networks. In: 2018 Eighth international conference on image processing theory, tools and applications (IPTA). IEEE, pp 1\u20136","DOI":"10.1109\/IPTA.2018.8608132"},{"issue":"8","key":"5565_CR167","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1109\/TIP.2018.2831899","volume":"27","author":"H Talebi","year":"2018","unstructured":"Talebi H, Milanfar P (2018) Nima: neural image assessment. IEEE Trans Image Process 27(8):3998\u20134011","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"5565_CR168","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1109\/TCSVT.2018.2886771","volume":"30","author":"W Zhang","year":"2018","unstructured":"Zhang W, Ma K, Yan J, Deng D, Wang Z (2018) Blind image quality assessment using a deep bilinear convolutional neural network. IEEE Trans Circuits Syst Video Technol 30(1):36\u201347. https:\/\/doi.org\/10.1109\/TCSVT.2018.2886771","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"5565_CR169","doi-asserted-by":"crossref","unstructured":"Verkoelen SD, Lamers MH, van der Putten P (2017) Exploring the exactitudes portrait series with restricted boltzmann machines. In: International conference on evolutionary and biologically inspired music and art. Springer, pp 321\u2013337","DOI":"10.1007\/978-3-319-55750-2_22"},{"issue":"5786","key":"5565_CR170","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"5565_CR171","unstructured":"Exactitude website. https:\/\/exactitudes.com\/collectie\/?v=s"},{"issue":"1","key":"5565_CR172","doi-asserted-by":"crossref","first-page":"011006","DOI":"10.1117\/1.3267105","volume":"19","author":"EC Larson","year":"2010","unstructured":"Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. J Electron Imaging 19(1):011006","journal-title":"J Electron Imaging"},{"issue":"1","key":"5565_CR173","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TIP.2015.2500021","volume":"25","author":"D Ghadiyaram","year":"2015","unstructured":"Ghadiyaram D, Bovik AC (2015) Massive online crowd sourced study of subjective and objective picture quality. IEEE Trans Image Process 25(1):372\u2013387","journal-title":"IEEE Trans Image Process"},{"key":"5565_CR174","doi-asserted-by":"crossref","unstructured":"Jayaraman D, Mittal A, Moorthy AK, Bovik AC (2012) Objective quality assessment of multiply distorted images. In: 2012 Conference record of the forty sixth asilomar conference on signals, systems and computers (ASILOMAR). IEEE, pp 1693\u20131697","DOI":"10.1109\/ACSSC.2012.6489321"},{"key":"5565_CR175","unstructured":"Ponomarenko N, Ieremeiev O, Lukin V, Egiazarian K, Jin L, Astola J, Vozel B, Chehdi K, Carli M, Battisti F et al (2013) Color image database tid2013: peculiarities and preliminary results. In: European workshop on visual information processing (EUVIP). IEEE, pp 106\u2013111"},{"key":"5565_CR176","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H, Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861"},{"key":"5565_CR177","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"issue":"11","key":"5565_CR178","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","volume":"15","author":"HR Sheikh","year":"2006","unstructured":"Sheikh HR, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15(11):3440\u20133451","journal-title":"IEEE Trans Image Process"},{"key":"5565_CR179","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.image.2014.10.009","volume":"30","author":"N Ponomarenko","year":"2015","unstructured":"Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, Battisti F et al (2015) Image database tid2013: peculiarities, results and perspectives. Sig Process Image Commun 30:57\u201377","journal-title":"Sig Process Image Commun"},{"issue":"2","key":"5565_CR180","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","volume":"26","author":"K Ma","year":"2016","unstructured":"Ma K, Duanmu Z, Wu Q, Wang Z, Yong H, Li H, Zhang L (2016) Waterloo exploration database: new challenges for image quality assessment models. IEEE Trans Image Process 26(2):1004\u20131016","journal-title":"IEEE Trans Image Process"},{"key":"5565_CR181","doi-asserted-by":"crossref","unstructured":"Carballal A, Perez R, Santos A, Castro L (2014) A complexity approach for identifying aesthetic composite landscapes. In: International conference on evolutionary and biologically inspired music and art. Springer, pp 50\u201361","DOI":"10.1007\/978-3-662-44335-4_5"},{"key":"5565_CR182","doi-asserted-by":"crossref","unstructured":"Lu X, Lin Z, Jin H, Yang J, Wang JZ (2014) Rapid: rating pictorial aesthetics using deep learning. In: Proceedings of the 22nd ACM international conference on Multimedia. pp 457\u2013466","DOI":"10.1145\/2647868.2654927"},{"issue":"7","key":"5565_CR183","first-page":"273","volume":"8","author":"Y Zhou","year":"2015","unstructured":"Zhou Y, Li G, Tan Y (2015) Computational aesthetics of photos quality assessment and classification based on artificial neural network with deep learning methods. Int J Signal Process Image Process Pattern Recognit 8(7):273\u2013282","journal-title":"Int J Signal Process Image Process Pattern Recognit"},{"key":"5565_CR184","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.neucom.2015.05.095","volume":"168","author":"Z Dong","year":"2015","unstructured":"Dong Z, Tian X (2015) Multi-level photo quality assessment with multi-view features. Neurocomputing 168:308\u2013319","journal-title":"Neurocomputing"},{"key":"5565_CR185","doi-asserted-by":"crossref","unstructured":"Campbell A, Ciesielksi V, Qin AK (2015) Feature discovery by deep learning for aesthetic analysis of evolved abstract images. In: International conference on evolutionary and biologically inspired music and art. Springer, pp 27\u201338","DOI":"10.1007\/978-3-319-16498-4_3"},{"key":"5565_CR186","unstructured":"Xu Q, D\u2019Souza D, Ciesielski V (2007) Evolving images for entertainment. In: Proceedings of the 4th Australasian conference on Interactive entertainment. RMIT University, p 26"},{"key":"5565_CR187","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.image.2016.05.009","volume":"47","author":"W Wang","year":"2016","unstructured":"Wang W, Zhao M, Wang L, Huang J, Cai C, Xu X (2016) A multi-scene deep learning model for image aesthetic evaluation. Signal Process Image Commun 47:511\u2013518","journal-title":"Signal Process Image Commun"},{"key":"5565_CR188","doi-asserted-by":"crossref","unstructured":"Jin X, Chi J, Peng S, Tian Y, Ye C, Li X (2016) Deep image aesthetics classification using inception modules and fine-tuning connected layer. In: 2016 8th international conference on wireless communications and signal processing (WCSP). IEEE, pp 1\u20136","DOI":"10.1109\/WCSP.2016.7752571"},{"key":"5565_CR189","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.image.2016.05.004","volume":"47","author":"Y Kao","year":"2016","unstructured":"Kao Y, Huang K, Maybank S (2016) Hierarchical aesthetic quality assessment using deep convolutional neural networks. Signal Process Image Commun 47:500\u2013510","journal-title":"Signal Process Image Commun"},{"key":"5565_CR190","unstructured":"Kao Y, He R, Huang K, Visual aesthetic quality assessment with multi-task deep learning. arXiv:1604.04970 5"},{"key":"5565_CR191","unstructured":"Malu G, Bapi RS, Indurkhya B, Learning photography aesthetics with deep cnns. arXiv:1707.03981"},{"key":"5565_CR192","doi-asserted-by":"crossref","unstructured":"Kong S, Shen X, Lin Z, Mech R, Fowlkes C (2016) Photo aesthetics ranking network with attributes and content adaptation. In: European conference on computer vision. Springer, pp 662\u2013679","DOI":"10.1007\/978-3-319-46448-0_40"},{"key":"5565_CR193","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.neucom.2016.08.098","volume":"228","author":"Y Tan","year":"2017","unstructured":"Tan Y, Tang P, Zhou Y, Luo W, Kang Y, Li G (2017) Photograph aesthetical evaluation and classification with deep convolutional neural networks. Neurocomputing 228:165\u2013175","journal-title":"Neurocomputing"},{"issue":"6","key":"5565_CR194","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s11801-017-7203-6","volume":"13","author":"Y-X Li","year":"2017","unstructured":"Li Y-X, Pu Y-Y, Xu D, Qian W-H, Wang L-P (2017) Image aesthetic quality evaluation using convolution neural network embedded learning. Optoelectron Lett 13(6):471\u2013475","journal-title":"Optoelectron Lett"},{"key":"5565_CR195","doi-asserted-by":"crossref","unstructured":"Lemarchand F et al (2017) From computational aesthetic prediction for images to films and online videos. AVANT. Pismo Awangardy Filozoficzno-Naukowej (S):69\u201378","DOI":"10.26913\/80s02017.0111.0007"},{"key":"5565_CR196","doi-asserted-by":"crossref","unstructured":"Tzelepis C, Mavridaki E, Mezaris V, Patras I (2016) Video aesthetic quality assessment using kernel support vector machine with isotropic gaussian sample uncertainty (ksvm-igsu). In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 2410\u20132414","DOI":"10.1109\/ICIP.2016.7532791"},{"key":"5565_CR197","unstructured":"Murray N, Gordo A, A deep architecture for unified aesthetic prediction. arXiv:1708.04890"},{"key":"5565_CR198","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\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5565_CR199","doi-asserted-by":"crossref","unstructured":"Bianco S, Celona L, Napoletano P, Schettini R (2016) Predicting image aesthetics with deep learning. In: International conference on advanced concepts for intelligent vision systems. Springer, pp 117\u2013125","DOI":"10.1007\/978-3-319-48680-2_11"},{"key":"5565_CR200","unstructured":"Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. In: Advances in neural information processing systems. pp 487\u2013495"},{"key":"5565_CR201","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.patrec.2018.05.016","volume":"112","author":"F Lemarchand","year":"2018","unstructured":"Lemarchand F (2018) Fundamental visual features for aesthetic classification of photographs across datasets. Pattern Recogn Lett 112:9\u201317","journal-title":"Pattern Recogn Lett"},{"key":"5565_CR202","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.image.2018.05.006","volume":"67","author":"C Zhang","year":"2018","unstructured":"Zhang C, Zhu C, Xu X, Liu Y, Xiao J, Tillo T (2018) Visual aesthetic understanding: sample-specific aesthetic classification and deep activation map visualization. Signal Process Image Commun 67:12\u201321","journal-title":"Signal Process Image Commun"},{"key":"5565_CR203","unstructured":"Zhang C, Zhu C, Xu X, Liu Y, Xiao J, Tillo T, Modelos de cnn de zhan c et al. https:\/\/github.com\/galoiszhang\/AWCU"},{"issue":"21","key":"5565_CR204","doi-asserted-by":"crossref","first-page":"14341","DOI":"10.1007\/s11042-018-6436-0","volume":"79","author":"X Jin","year":"2020","unstructured":"Jin X, Wu L, Zhao G, Zhou X, Zhang X, Li X (2020) IDEA: a new dataset for image aesthetic scoring. Multimed Tools Appl 79(21):14341\u201314355","journal-title":"Multimed Tools Appl"},{"key":"5565_CR205","unstructured":"Jin X, Wu L, Zhao G, Zhou X, Zhang X, Li X (2018) Photo aesthetic scoring through spatial aggregation perception dcnn on a new idea dataset. In: International symposium on artificial intelligence and robotics. Springer, pp 41\u201350"},{"key":"5565_CR206","doi-asserted-by":"crossref","unstructured":"Apostolidis K, Mezaris V (2019) Image aesthetics assessment using fully convolutional neural networks. In: International conference on multimedia modeling. Springer, pp 361\u2013373","DOI":"10.1007\/978-3-030-05710-7_30"},{"key":"5565_CR207","unstructured":"Keras neural network api. https:\/\/keras.io\/"},{"key":"5565_CR208","unstructured":"Implementation en keras neural network api. https:\/\/github.com\/bmezaris\/fullyconvolutionalnetworks"},{"key":"5565_CR209","unstructured":"Sheng K, Dong W, Chai M, Wang G, Zhou P, Huang F, Hu B-G, Ji R, Ma C, Revisiting image aesthetic assessment via self-supervised feature learning. arXiv:1911.11419"},{"key":"5565_CR210","doi-asserted-by":"publisher","first-page":"5889","DOI":"10.1007\/s00521-019-04065-4","volume":"32","author":"A Carballal","year":"2019","unstructured":"Carballal A, Fernandez-Lozano C, Heras J, Romero J (2019) Transfer learning features for predicting aesthetics through a novel hybrid machine learning method. Neural Comput Appl 32:5889\u20135900. https:\/\/doi.org\/10.1007\/s00521-019-04065-4","journal-title":"Neural Comput Appl"},{"key":"5565_CR211","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\u201373710","journal-title":"IEEE Access"},{"key":"5565_CR212","unstructured":"Dai Y, Cnn-based repetitive self-revised learning for photos\u2019 aesthetics imbalanced classification. arXiv:2003.03081"},{"key":"5565_CR213","unstructured":"Dai Y, Sample-specific repetitive learning for photo aesthetic assessment and highlight region extraction. arXiv:1909.08213"},{"key":"5565_CR214","unstructured":"500px. https:\/\/web.500px.com\/"},{"key":"5565_CR215","doi-asserted-by":"crossref","unstructured":"Semmo A, Isenberg T, D\u00f6llner J (2017) Neural style transfer: a paradigm shift for image-based artistic rendering?. In: Proceedings of the symposium on non-photorealistic animation and rendering. pp 1\u201313","DOI":"10.1145\/3092919.3092920"},{"key":"5565_CR216","unstructured":"Gatys L, Ecker AS, Bethge M (2015) Texture synthesis using convolutional neural networks. In: Advances in neural information processing systems. pp 262\u2013270"},{"issue":"1","key":"5565_CR217","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1023\/A:1026553619983","volume":"40","author":"J Portilla","year":"2000","unstructured":"Portilla J, Simoncelli EP (2000) A parametric texture model based on joint statistics of complex wavelet coefficients. Int J Comput Vis 40(1):49\u201370","journal-title":"Int J Comput Vis"},{"key":"5565_CR218","unstructured":"Gatys LA, Ecker AS, Bethge M, A neural algorithm of artistic style. arXiv:1508.06576"},{"key":"5565_CR219","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\u20132423","DOI":"10.1109\/CVPR.2016.265"},{"key":"5565_CR220","unstructured":"Gatys LA, Bethge M, Hertzmann A, Shechtman E, Preserving color in neural artistic style transfer. arXiv:1606.05897"},{"key":"5565_CR221","doi-asserted-by":"crossref","unstructured":"Chen Y-L, Hsu C-T (2016) Towards deep style transfer: a content-aware perspective. In: BMVC","DOI":"10.5244\/C.30.8"},{"key":"5565_CR222","unstructured":"Champandard AJ, Semantic style transfer and turning two-bit doodles into fine artworks. arXiv:1603.01768"},{"key":"5565_CR223","doi-asserted-by":"crossref","unstructured":"Li C, Wand M (2016) Combining markov random fields and convolutional neural networks for image synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2479\u20132486","DOI":"10.1109\/CVPR.2016.272"},{"key":"5565_CR224","doi-asserted-by":"crossref","unstructured":"Joshi B, Stewart K, Shapiro D (2017) Bringing impressionism to life with neural style transfer in come swim. In: Proceedings of the ACM SIGGRAPH digital production symposium. pp 1\u20135","DOI":"10.1145\/3105692.3105697"},{"key":"5565_CR225","doi-asserted-by":"crossref","unstructured":"Chen Y, Lai Y-K, Liu Y-J (2017) Transforming photos to comics using convolutional neural networks. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 2010\u20132014","DOI":"10.1109\/ICIP.2017.8296634"},{"key":"5565_CR226","unstructured":"Krishnan U, Sharma A, Chattopadhyay P, Feature fusion from multiple paintings for generalized artistic style transfer. Available at SSRN 3387817"},{"issue":"11\u00de","key":"5565_CR227","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TVCG.2019.2921336","volume":"26","author":"Y Jing","year":"2020","unstructured":"Jing Y, Yang Y, Feng Z, Ye J, Yu Y, Song M (2020) Neural style transfer: a review. IEEE Trans Visual Comput Graph 26(11):3365\u20133385. https:\/\/doi.org\/10.1109\/TVCG.2019.2921336","journal-title":"IEEE Trans Visual Comput Graph"},{"key":"5565_CR228","unstructured":"Portfolio. greg surma. https:\/\/gsurma.github.io\/"},{"key":"5565_CR229","unstructured":"Correia J, Martins T, Martins P, Machado P (2016) X-faces: the exploit is out there. In: Proceedings of the seventh international conference on computational creativity"},{"key":"5565_CR230","doi-asserted-by":"crossref","unstructured":"Machado P, Correia J, Romero J (2012) Improving face detection. In: European conference on genetic programming. Springer, pp 73\u201384","DOI":"10.1007\/978-3-642-29139-5_7"},{"key":"5565_CR231","doi-asserted-by":"crossref","unstructured":"Machado P, Correia J, Romero J (2012) Expression-based evolution of faces. In: International conference on evolutionary and biologically inspired music and art. Springer, pp 187\u2013198","DOI":"10.1007\/978-3-642-29142-5_17"},{"key":"5565_CR232","doi-asserted-by":"crossref","unstructured":"Correia J, Martins T, Machado P (2019) Evolutionary data augmentation in deep face detection. In: Proceedings of the genetic and evolutionary computation conference companion. pp 163\u2013164","DOI":"10.1145\/3319619.3322053"},{"key":"5565_CR233","doi-asserted-by":"crossref","unstructured":"Machado P, Vinhas A, Correia J, Ek\u00e1rt A (2015) Evolving ambiguous images. In: Twenty-fourth international joint conference on artificial intelligence","DOI":"10.1145\/2813536.2813539"},{"key":"5565_CR234","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","volume":"124","author":"C Tian","year":"2020","unstructured":"Tian C, Xu Y, Li Z, Zuo W, Fei L, Liu H (2020) Attention-guided CNN for image denoising. Neural Netw 124:117\u2013129. https:\/\/doi.org\/10.1016\/j.neunet.2019.12.024","journal-title":"Neural Netw"},{"key":"5565_CR235","unstructured":"Adnet, URL: http:\/\/github.com\/hellloxiaotian\/ADNet"},{"key":"5565_CR236","unstructured":"Colton S, Halskov J, Ventura D, Gouldstone I, Cook M, Ferrer BP (2015) The painting fool sees! new projects with the automated painter. In: ICCC. pp 189\u2013196"},{"key":"5565_CR237","unstructured":"Krzeczkowska A, El-Hage J, Colton S, Clark S (2010) Automated collage generation-with intent. In: ICCC. pp 36\u201340"},{"key":"5565_CR238","doi-asserted-by":"crossref","unstructured":"Colton S (2008) Automatic invention of fitness functions with application to scene generation. In: Workshops on applications of evolutionary computation. Springer, pp 381\u2013391","DOI":"10.1007\/978-3-540-78761-7_41"},{"key":"5565_CR239","unstructured":"Colton S (2008) Experiments in constraint-based automated scene generation. In: Proceedings of the 5th international joint workshop on computational creativity. pp 127\u2013136"},{"key":"5565_CR240","unstructured":"Colton S, Ferrer BP (2012) No photos harmed\/growing paths from seed: an exhibition. In: Proceedings of the symposium on non-photorealistic animation and rendering. pp 1\u201310"},{"key":"5565_CR241","doi-asserted-by":"crossref","unstructured":"Colton S (2012) Evolving a library of artistic scene descriptors. In: International conference on evolutionary and biologically inspired music and art. Springer, pp 35\u201347","DOI":"10.1007\/978-3-642-29142-5_4"},{"key":"5565_CR242","unstructured":"The painting fool. about me. http:\/\/www.thepaintingfool.com\/about\/index.html"},{"key":"5565_CR243","unstructured":"Colton S, Ventura D (2014) You can\u2019t know my mind: a festival of computational creativity. In: ICCC. pp 351\u2013354"},{"key":"5565_CR244","unstructured":"Dataset darci. http:\/\/darci.cs.byu.edu"},{"key":"5565_CR245","unstructured":"Radford A, Metz L, Chintala S, Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434"},{"key":"5565_CR246","unstructured":"Yu F, Seff A, Zhang Y, Song S, Funkhouser T, Xiao J, Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv:1506.03365"},{"key":"5565_CR247","unstructured":"Dbpedia. https:\/\/wiki.dbpedia.org\/"},{"key":"5565_CR248","unstructured":"Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images"},{"key":"5565_CR249","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\u20133764","DOI":"10.1109\/ICIP.2017.8296985"},{"key":"5565_CR250","unstructured":"Elgammal A, Liu B, Elhoseiny M, Mazzone M, Can: creative adversarial networks, generating \u201cart\u201d by learning about styles and deviating from style norms. arXiv:1706.07068"},{"key":"5565_CR251","doi-asserted-by":"crossref","unstructured":"Neumann A, Pyromallis C, Alexander B (2018) Evolution of images with diversity and constraints using a generative adversarial network. In: International conference on neural information processing. Springer, pp 452\u2013465","DOI":"10.1007\/978-3-030-04224-0_39"},{"key":"5565_CR252","unstructured":"Neumann A, Pyromallis C, Alexander B, Evolution of images with diversity and constraints using a generator network. arXiv:1802.05480"},{"key":"5565_CR253","doi-asserted-by":"crossref","unstructured":"Talebi H, Milanfar P (2018) Learned perceptual image enhancement. In: 2018 IEEE international conference on computational photography (ICCP). IEEE, pp 1\u201313","DOI":"10.1109\/ICCPHOT.2018.8368474"},{"key":"5565_CR254","unstructured":"Bychkovsky V, Paris S, Chan E, Durand F (2011) Learning photographic global tonal adjustment with a database of input\/output image pairs. In: CVPR 2011. IEEE, pp 97\u2013104"},{"key":"5565_CR255","doi-asserted-by":"crossref","unstructured":"Bontrager P, Lin W, Togelius J, Risi S (2018) Deep interactive evolution. In: International conference on computational intelligence in music, sound, art and design. Springer, pp 267\u2013282","DOI":"10.1007\/978-3-319-77583-8_18"},{"key":"5565_CR256","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X, Tang X (2015) Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision. pp 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"key":"5565_CR257","doi-asserted-by":"crossref","unstructured":"Yu A, Grauman K (2014) Fine-grained visual comparisons with local learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 192\u2013199","DOI":"10.1109\/CVPR.2014.32"},{"key":"5565_CR258","doi-asserted-by":"crossref","unstructured":"Aubry M, Maturana D, Efros AA, Russell BC, Sivic J (2014) Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3762\u20133769","DOI":"10.1109\/CVPR.2014.487"},{"key":"5565_CR259","unstructured":"Van Noord N, Postma E, Light-weight pixel context encoders for image inpainting. arXiv:1801.05585"},{"key":"5565_CR260","doi-asserted-by":"crossref","unstructured":"Pathak D, Krahenbuhl P, Donahue J, Darrell T, Efros AA (2016) Context encoders: feature learning by inpainting. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2536\u20132544","DOI":"10.1109\/CVPR.2016.278"},{"key":"5565_CR261","doi-asserted-by":"crossref","unstructured":"Tanjil F, Ross BJ (2019) Deep learning concepts for evolutionary art. In: International conference on computational intelligence in music, sound, art and design (Part of EvoStar). Springer, pp 1\u201317","DOI":"10.1007\/978-3-030-16667-0_1"},{"key":"5565_CR262","unstructured":"Deng J, Berg A, Satheesh S, Su H, Khosla A, Li F (2012) Large scale visual recognition challenge 2012. In: ILSVRC 2012 workshop"},{"issue":"1","key":"5565_CR263","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1511\/2019.107.1.18","volume":"107","author":"A Elgammal","year":"2019","unstructured":"Elgammal A (2019) Ai is blurring the definition of artist: advanced algorithms are using machine learning to create art autonomously. Am Sci 107(1):18\u201322","journal-title":"Am Sci"},{"key":"5565_CR264","unstructured":"Aican.io. https:\/\/www.aican.io"},{"key":"5565_CR265","doi-asserted-by":"crossref","unstructured":"Blair A (2019) Adversarial evolution and deep learning\u2014How does an artist play with our visual system?. In: International conference on computational intelligence in music, sound, art and design (part of EvoStar). Springer, pp 18\u201334","DOI":"10.1007\/978-3-030-16667-0_2"},{"key":"5565_CR266","unstructured":"Shen X, Darmon F, Efros AA, Aubry M, Ransac-flow: generic two-stage image alignment. arXiv:2004.01526"},{"key":"5565_CR267","doi-asserted-by":"crossref","unstructured":"Shen X, Darmon F, Efros AA, Aubry M (2020) Ransac-flow: generic two-stage image alignment. In: 16th European conference on computer vision","DOI":"10.1007\/978-3-030-58548-8_36"},{"key":"5565_CR268","doi-asserted-by":"crossref","unstructured":"Barath D, Matas J (2018) Graph-cut ransac. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 6733\u20136741","DOI":"10.1109\/CVPR.2018.00704"},{"key":"5565_CR269","doi-asserted-by":"crossref","unstructured":"Barath D, Matas J, Noskova J (2019) Magsac: marginalizing sample consensus. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 10197\u201310205","DOI":"10.1109\/CVPR.2019.01044"},{"issue":"6","key":"5565_CR270","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381\u2013395","journal-title":"Commun ACM"},{"key":"5565_CR271","unstructured":"Pl\u00f6tz T, Roth S (2018) Neural nearest neighbors networks. In: Advances in neural information processing systems. pp 1087\u20131098"},{"key":"5565_CR272","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 652\u2013660"},{"issue":"8","key":"5565_CR273","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1109\/TPAMI.2012.257","volume":"35","author":"R Raguram","year":"2012","unstructured":"Raguram R, Chum O, Pollefeys M, Matas J, Frahm J-M (2012) Usac: a universal framework for random sample consensus. IEEE Trans Pattern Anal Mach Intell 35(8):2022\u20132038","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5565_CR274","doi-asserted-by":"crossref","unstructured":"Ranftl R, Koltun V (2018) Deep fundamental matrix estimation. In: Proceedings of the European conference on computer vision (ECCV). pp 284\u2013299","DOI":"10.1007\/978-3-030-01246-5_18"},{"key":"5565_CR275","doi-asserted-by":"crossref","unstructured":"Zhang J, Sun D, Luo Z, Yao A, Zhou L, Shen T, Chen Y, Quan L, Liao H (2019) Learning two-view correspondences and geometry using order-aware network. In: Proceedings of the IEEE international conference on computer vision. pp 5845\u20135854","DOI":"10.1109\/ICCV.2019.00594"},{"key":"5565_CR276","unstructured":"Jason JY, Harley AW, Derpanis KG (2016) Back to basics: unsupervised learning of optical flow via brightness constancy and motion smoothness. In: European conference on computer vision. Springer, pp 3\u201310"},{"issue":"4","key":"5565_CR277","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"5565_CR278","doi-asserted-by":"crossref","unstructured":"Yin Z, Shi J (2018) Geonet: unsupervised learning of dense depth, optical flow and camera pose. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1983\u20131992","DOI":"10.1109\/CVPR.2018.00212"},{"key":"5565_CR279","unstructured":"Temizel A et al (2018) Paired 3d model generation with conditional generative adversarial networks. In: Proceedings of the European conference on computer vision (ECCV)"},{"key":"5565_CR280","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1912\u20131920"},{"issue":"2","key":"5565_CR281","doi-asserted-by":"crossref","first-page":"697","DOI":"10.2991\/ijcis.d.190617.001","volume":"12","author":"H Li","year":"2019","unstructured":"Li H, Zheng Y, Wu X, Cai Q (2019) 3d Model generation and reconstruction using conditional generative adversarial network. Int J Comput Intell Syst 12(2):697\u2013705","journal-title":"Int J Comput Intell Syst"},{"key":"5565_CR282","unstructured":"Chang AX, Funkhouser T, Guibas L, Hanrahan P, Huang Q, Li Z, Savarese S, Savva M, Song S, Su H et al Shapenet: an information-rich 3d model repository. arXiv:1512.03012"},{"key":"5565_CR283","doi-asserted-by":"crossref","unstructured":"Lim JJ, Pirsiavash H, Torralba A (2013) Parsing ikea objects: fine pose estimation. In: Proceedings of the IEEE international conference on computer vision. pp 2992\u20132999","DOI":"10.1109\/ICCV.2013.372"},{"key":"5565_CR284","doi-asserted-by":"crossref","unstructured":"Volz V, Schrum J, Liu J, Lucas SM, Smith A, Risi S (2018) Evolving mario levels in the latent space of a deep convolutional generative adversarial network. In: Proceedings of the genetic and evolutionary computation conference. pp 221\u2013228","DOI":"10.1145\/3205455.3205517"},{"key":"5565_CR285","unstructured":"Summerville AJ, Snodgrass S, Mateas M, Ontan\u00f3n S, The vglc: the video game level corpus. arXiv:1606.07487"},{"key":"5565_CR286","doi-asserted-by":"crossref","unstructured":"Togelius J, Karakovskiy S, Baumgarten R (2010) The 2009 mario ai competition. In: IEEE congress on evolutionary computation. IEEE, pp 1\u20138","DOI":"10.1109\/CEC.2010.5586133"},{"key":"5565_CR287","doi-asserted-by":"crossref","unstructured":"Hollingsworth B, Schrum J (2019) Infinite art gallery: a game world of interactively evolved artwork. In: 2019 IEEE congress on evolutionary computation (CEC). IEEE, pp 474\u2013481","DOI":"10.1109\/CEC.2019.8790370"},{"key":"5565_CR288","unstructured":"Romero J, Automatic real estate image evaluation by artificial intelligence. Present. https:\/\/cutt.ly\/DfVT6VI"},{"key":"5565_CR289","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM (2006) Pattern recognition and machine learning. springer, Berlin"},{"key":"5565_CR290","volume-title":"Analyzing microarray gene expression data","author":"GJ McLachlan","year":"2005","unstructured":"McLachlan GJ, Do K-A, Ambroise C (2005) Analyzing microarray gene expression data, vol 422. Wiley, New York"},{"issue":"2","key":"5565_CR291","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/aic.690370209","volume":"37","author":"MA Kramer","year":"1991","unstructured":"Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37(2):233\u2013243","journal-title":"AIChE J"},{"key":"5565_CR292","volume-title":"Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems","author":"A G\u00e9ron","year":"2019","unstructured":"G\u00e9ron A (2019) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. O\u2019Reilly Media, Newton"},{"key":"5565_CR293","unstructured":"Lin T-Y, RoyChowdhury A, Maji S (2015) Bilinear cnn models for fine-grained visual recognition. In: Proceedings of the IEEE international conference on computer vision. pp 1449\u20131457"},{"key":"5565_CR294","unstructured":"Kingma DP, Ba J, Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"5565_CR295","doi-asserted-by":"crossref","DOI":"10.1561\/9781601982957","volume-title":"Learning deep architectures for AI","author":"Y Bengio","year":"2009","unstructured":"Bengio Y (2009) Learning deep architectures for AI. Now Publishers Inc, New York"},{"key":"5565_CR296","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems. pp 2672\u20132680"},{"key":"5565_CR297","doi-asserted-by":"crossref","unstructured":"Hochreiter S, Ja1 4 rgen schmidhuber (1997) \u201clong short-term memory\u201d. Neural Comput 9(8)","DOI":"10.1162\/neco.1997.9.8.1735"},{"issue":"02","key":"5565_CR298","doi-asserted-by":"crossref","first-page":"1930001","DOI":"10.1142\/S0218213019300011","volume":"28","author":"NA Barriga","year":"2019","unstructured":"Barriga NA (2019) A short introduction to procedural content generation algorithms for videogames. Int J Artif Intell Tools 28(02):1930001","journal-title":"Int J Artif Intell Tools"},{"key":"5565_CR299","unstructured":"Togelius J, Kastbjerg E, Schedl D, Yannakakis GN (2011) What is procedural content generation? Mario on the borderline. In: Proceedings of the 2nd international workshop on procedural content generation in games. pp 1\u20136"},{"issue":"4","key":"5565_CR300","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427\u2013437","journal-title":"Inf Process Manag"},{"key":"5565_CR301","unstructured":"Smolensky P (1986) Information processing in dynamical systems: foundations of harmony theory, Technical report. Colorado University at Boulder Department of Computer Science"},{"issue":"3","key":"5565_CR302","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"5565_CR303","doi-asserted-by":"crossref","unstructured":"Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2008) Lost in quantization: improving particular object retrieval in large scale image databases. In: 2008 IEEE conference on computer vision and pattern recognition. IEEE, pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587635"},{"key":"5565_CR304","doi-asserted-by":"crossref","unstructured":"Ren J, Shen X, Lin Z, Mech R, Foran DJ (2017) Personalized image aesthetics. In: Proceedings of the IEEE international conference on computer vision. pp 638\u2013647","DOI":"10.1109\/ICCV.2017.76"},{"key":"5565_CR305","doi-asserted-by":"crossref","unstructured":"You Q, Luo J, Jin H, Yang J (2015) Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Twenty-ninth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9179"},{"key":"5565_CR306","doi-asserted-by":"crossref","unstructured":"Katsurai M, Satoh S (2016) Image sentiment analysis using latent correlations among visual, textual, and sentiment views. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2837\u20132841","DOI":"10.1109\/ICASSP.2016.7472195"},{"key":"5565_CR307","doi-asserted-by":"crossref","unstructured":"Khosla A, Raju AS, Torralba A, Oliva A (2015) Understanding and predicting image memorability at a large scale. In: Proceedings of the IEEE international conference on computer vision, pp 2390\u20132398","DOI":"10.1109\/ICCV.2015.275"},{"key":"5565_CR308","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)"},{"key":"5565_CR309","doi-asserted-by":"crossref","unstructured":"Yanulevskaya V, Uijlings J, Bruni E, Sartori A, Zamboni E, Bacci F, Melcher D, Sebe N (2012) In the eye of the beholder: employing statistical analysis and eye tracking for analyzing abstract paintings. In: Proceedings of the 20th ACM international conference on multimedia. pp 349\u2013358","DOI":"10.1145\/2393347.2393399"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05565-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-020-05565-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05565-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T05:45:18Z","timestamp":1670737518000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-020-05565-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":309,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["5565"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05565-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"22 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declares that there are no conflicts of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No code was used to support this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}