Using Artificial Intelligence Techniques to Emulate the Creativity of a Portrait Painter

We present three new machine learning based artificial intelligence (AI) techniques, which we have added to our parameterised computational painterly rendering framework and show their benefits in computational creativity and non-photorealistic rendering (NPR) of portraits. Traditional portrait artists use a specific but open human creativity, vision, technical and perception methodologies to create a painterly portrait of a live or photographed sitter. By incorporating more open ended creative, semantic and concept blending techniques, these new neural network based AI techniques allow us to better model the creative cognitive thinking process that human painters employ. We analyse these AI based methods for their operating principles and outputs that together along with our parameterised NPR modules can be relevant to the field of computational creativity research and computational painterly rendering.

Content

Author and article information

Contributors

Steve DiPaola

Graeme McCaig

Conference

Publication date: July 2016

Publication date (Print): July 2016

Pages: 158-165

Affiliations

[1 ] Simon Fraser University

Canada

Article

DOI: 10.14236/ewic/EVA2016.32

SO-VID: 836b39f0-2cfd-45fe-8770-5560d43adb4e

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Electronic Visualisation and the Arts

Conference acronym: EVA

Conference number:

Conference location: London, UK

Conference date: 12 - 14 July 2016

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Electronic Visualisation and the Arts

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2016.32

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Y. Bengio 2009 Learning deep architectures for AI. Foundations and Trends in Machine Learning 2 1 1 127
M. A. Boden 2004 The creative mind: Myths and mechanisms Psychology Press
P. Cavanagh 2005 The artist as neuroscientist Nature 434 7031 301 307 17 March
J. DiCarloD. ZoccolanN. Rust 2012 How does the brain solve visual object recognition? Neuron 73 3 415 434
S. DiPaola 2007 A Knowledge Based Approach to Modelling Portrait Painting Methodology Electronic Visualisation and the Arts EVA London, UK
S. DiPaola 2009 Exploring a Parameterized Portrait Painting Space International Journal of Art and Technology 2 1-2 82 93
S. DiPaolaS. Salevati 2014 Using a Creative Evolutionary System for Experiencing the Art Electronic Imaging & the Visual Arts Italy 88 93
S. DiPaola, R. McCaig, K. Carson,S. Salevati, 2013 Adaptation of an Autonomous Creative Evolutionary System for Real-World Design Application Based on Creative Cognition Computational Creativity (ICCC) 40 47
L. Gabora 2010 Revenge of the “neurds”: Characterizing creative thought in terms of the structure and dynamics of memory Creativity Research Journal 22 1 1 13
L. A. GatysA. S. EckerM. Bethge 2015 A neural algorithm of artistic style.arXiv:1508.06576
B. GoochA. Gooch 2001 Non-Photorealistic Rendering AK Peters, Ltd
Y. JiaE. ShelhamerJ. DonahueS. KarayevJ. LongR. GirshickT. Darrell 2014 Caffe: Convolutional architecture for fast feature embedding In Proceedings of the ACM International Conference on Multimedia 675 678 ACM
A. KrizhevskyI. SutskeverG. E. Hinton 2012 ImageNet Classification with DeepConvolutional Neural Networks NIPS 1 4
Q. V. LeM. RanzatoR. MongaM. DevinK. ChenG. CorradoA. Y. Ng 2013 Building high-level features using large scale unsupervised learning In Acoustics, Speech and Signal Processing (ICASSP) 8595 8598
Y. LeCunL. BottouY. BengioP. Haffner 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE 86 11 2278 2324
G. McCaigS. DiPaolaL. Gabora 2016 Deep Convolutional Networks as Models of Generalization and Blending within Visual Creativity In Submission
A. Mordvintsev et al. 2015 Online Blog ext-link-type="uri" xlink:href="http://googleresearch.blogspot.ca/2015/06/inceptionism-going-deeper-into-neural.html">http://googleresearch.blogspot.ca/2015/06/inceptionism-going-deeper-into-neural.html
R. SalakhutdinovG. E. Hinton 2009 Deep boltzmann machines In Proc Artificial Intelligence and Statistics 5 448 455
A. SantellaDecarlo 2002 Abstracted painterly renderings using eye-tracking data NPAR ‘02 Proceedings of Non-photorealistic animation and rendering 75 ACM
K. SimonyanA. Zisserman 2014 Very deep convolutional networks for large-scale image recognition.arXiv Preprint arXiv:1409.1556
C. SzegedyW. LiuY. JiaP. SermanetS. ReedD. AnguelovA. Rabinovich 2015 Going deeper with convolutions Proceedings of Computer Vision and Pattern Recognition 1 9
D. VanderhaegheJ. Collomosse 2013 Stroke Based Painterly Rendering Image and Video-Based Artistic Stylisation 3 21 Springer
S. Zeki 2001 Essays on Science and Society: Artistic Creativity and the Brain Science 293 5527 51 52
K. ZengM. ZhaoC. Xiong,S.C. Zhu 2009 From image parsing to painterly rendering ACM Trans. Graph. 29 1 2 1 2 11

Comments

Comment on this article

[1] Y. Bengio 2009 Learning deep architectures for AI. Foundations and Trends in Machine Learning 2 1 1 127

[2] M. A. Boden 2004 The creative mind: Myths and mechanisms Psychology Press

[3] P. Cavanagh 2005 The artist as neuroscientist Nature 434 7031 301 307 17 March

[4] J. DiCarloD. ZoccolanN. Rust 2012 How does the brain solve visual object recognition? Neuron 73 3 415 434

[5] S. DiPaola 2007 A Knowledge Based Approach to Modelling Portrait Painting Methodology Electronic Visualisation and the Arts EVA London, UK

[6] S. DiPaola 2009 Exploring a Parameterized Portrait Painting Space International Journal of Art and Technology 2 1-2 82 93

[7] S. DiPaolaS. Salevati 2014 Using a Creative Evolutionary System for Experiencing the Art Electronic Imaging & the Visual Arts Italy 88 93

[8] S. DiPaola, R. McCaig, K. Carson,S. Salevati, 2013 Adaptation of an Autonomous Creative Evolutionary System for Real-World Design Application Based on Creative Cognition Computational Creativity (ICCC) 40 47

[9] L. Gabora 2010 Revenge of the “neurds”: Characterizing creative thought in terms of the structure and dynamics of memory Creativity Research Journal 22 1 1 13

[10] L. A. GatysA. S. EckerM. Bethge 2015 A neural algorithm of artistic style.arXiv:1508.06576

[11] B. GoochA. Gooch 2001 Non-Photorealistic Rendering AK Peters, Ltd

[12] Y. JiaE. ShelhamerJ. DonahueS. KarayevJ. LongR. GirshickT. Darrell 2014 Caffe: Convolutional architecture for fast feature embedding In Proceedings of the ACM International Conference on Multimedia 675 678 ACM

[13] A. KrizhevskyI. SutskeverG. E. Hinton 2012 ImageNet Classification with DeepConvolutional Neural Networks NIPS 1 4

[14] Q. V. LeM. RanzatoR. MongaM. DevinK. ChenG. CorradoA. Y. Ng 2013 Building high-level features using large scale unsupervised learning In Acoustics, Speech and Signal Processing (ICASSP) 8595 8598

[15] Y. LeCunL. BottouY. BengioP. Haffner 1998 Gradient-based learning applied to document recognition Proceedings of the IEEE 86 11 2278 2324

[16] G. McCaigS. DiPaolaL. Gabora 2016 Deep Convolutional Networks as Models of Generalization and Blending within Visual Creativity In Submission

[17] A. Mordvintsev et al. 2015 Online Blog ext-link-type="uri" xlink:href="http://googleresearch.blogspot.ca/2015/06/inceptionism-going-deeper-into-neural.html">http://googleresearch.blogspot.ca/2015/06/inceptionism-going-deeper-into-neural.html

[18] R. SalakhutdinovG. E. Hinton 2009 Deep boltzmann machines In Proc Artificial Intelligence and Statistics 5 448 455

[19] A. SantellaDecarlo 2002 Abstracted painterly renderings using eye-tracking data NPAR ‘02 Proceedings of Non-photorealistic animation and rendering 75 ACM

[20] K. SimonyanA. Zisserman 2014 Very deep convolutional networks for large-scale image recognition.arXiv Preprint arXiv:1409.1556

[21] C. SzegedyW. LiuY. JiaP. SermanetS. ReedD. AnguelovA. Rabinovich 2015 Going deeper with convolutions Proceedings of Computer Vision and Pattern Recognition 1 9

[22] D. VanderhaegheJ. Collomosse 2013 Stroke Based Painterly Rendering Image and Video-Based Artistic Stylisation 3 21 Springer

[23] S. Zeki 2001 Essays on Science and Society: Artistic Creativity and the Brain Science 293 5527 51 52

[24] K. ZengM. ZhaoC. Xiong,S.C. Zhu 2009 From image parsing to painterly rendering ACM Trans. Graph. 29 1 2 1 2 11

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