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New Method for Museum Archiving: “Quantitative Analysis Meets Art History”

Published: 06 December 2022 Publication History

Abstract

As museums are encouraged to explore new ways to generate digital content, and quantitative methods are being used to suggest new angles and important analysis tools for art-historical research and museum archives. Recent advances in digital image processing provide valuable data to describe the content of an artwork and support all the parties concerned with the authentication, appreciation, preservation, and archiving of artworks. Particularly, identification of artist-specific indicia or classification of the artistic style of paintings must be performed for indexing large artwork databases. Thus, we determined primary criteria based on feature-based analysis and suggested a new archiving framework for applications that enable collection, management, and visualization of artworks.

References

[1]
D. Stork. 2009. Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature. In Proceedings of the International Conference on Computer Analysis of Images and Patterns (CAIP'09). Springer, Berlin, 9–24. DOI:https://doi.org/10.1007/978-3-642-03767-2_2
[2]
M. Bressan, C. Cifarelli, and F. Perronnin. 2008. An analysis of the relationship between painters based on their work. In Proceedings of the 15th IEEE International Conference on Image Processing. IEEE, San Diego, CA, 113–116. DOI:https://doi.org/10.1109/ICIP.2008.4711704
[3]
I. Widjaja, W. K. Leow, and F. C. Wu. 2003. Identifying painters from color profiles of skin patches in painting images. In Proceedings of the 2003 International Conference on Image Processing. IEEE, Barcelona, 845–848. DOI:https://doi.org/10.1109/ICIP.2003.1247095
[4]
S. Lyu, D. Rockmore, and H. Farid. 2004. A digital technique for art authentication. Proceedings of the National Academy of Sciences 101, 49 (2004), 17006–17010. DOI:https://doi.org/10.1073/pnas.0406398101
[5]
H. Jaap van den Herik and O. P. Eric. 2000. Discovering the visual signature of painters. Future Directions for Intelligent Systems and Information Sciences, Studies in Fuzziness and Soft Computing Book Series Vol. 45, Springer, 129–147. DOI:https://doi.org/10.1007/978-3-7908-1856-7-7
[6]
M. Kim and J. Kim. 2019. Complementary quantitative approach to unsolved issues in art history: Similarity of visual features in the paintings of vermeer and his probable mentors. Leonardo 52, 2 (2019), 164–174. DOI:https://doi.org/10.1162/leon_a_01401
[7]
A. Elgammal, M. Mazzone, B. Liu, D. Kim, and M. Elhoseiny. 2018. The shape of art history in the eyes of the machine. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 32, 1 (2018), 2183–2191. DOI:
[8]
D. Kim, S. Son, and H. Jeong. 2014. Large-scale quantitative analysis of painting arts. Scientific reports, Nature Research 4, 1 (2014), 1–7. DOI:https://doi.org/10.1038/srep07370
[9]
B. Lee, D. Kim, S. Sun, H. Jeong, and J. Park. 2018. Heterogeneity in chromatic distance in images and characterization of massive painting data set. PLoS ONE 13, 9 (2018), e0204430. DOI:
[10]
J. M. Hughes, D. J. Graham, and D. N. Rockmore. 2010. Quantification of artistic style through sparse coding analysis in the drawings of pieter bruegel the elder. Proceedings of the National Academy of Sciences 107, 4 (2010), 1279–1283. DOI:https://doi.org/10.1073/pnas.0910530107
[11]
H. Qi and H. Hughes. 2011. A new method for visual stylometry on impressionist paintings. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, Prague, 2036–2039. DOI:https://doi.org/10.1109/ICASSP.2011.5946912
[12]
R. J. Sethi, C. A. Buell, W. P. Seeley, and Y. Gil. 2018. Intelligent workflows for visual stylometry. AI Matters 3, 4 (2018), 14–17. DOI:https://doi.org/10.1145/3175502.3175507
[13]
J. Li, L. Yao, E. Hendriks, and J. Z. Wang. 2012. Rhythmic brushstrokes distinguish van gogh from his contemporaries: Findings via automated brushstroke extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 6 (2012), 1159–1176. DOI:https://doi.org/10.1109/TPAMI.2011.203
[14]
F. Lamberti, A. Sanna, and G. Paravati. 2014. Computer-assisted analysis of painting brushstrokes: Digital image processing for unsupervised extraction of visible features from van Gogh's works. EURASIP Journal on Image and Video Processing 1, 53 (2014), 1–17. DOI:https://doi.org/10.1186/1687-5281-2014-53
[15]
M. Kim and J. Kim. 2017. Comparative analysis of dutch art and impressionism through an interdisciplinary method. In Proceedings of the IEEE International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. IEEE, Shanghai, 1–6. DOI:https://doi.org/10.1109/CISP-BMEI.2017.8302048
[16]
B. H. Berrie. 2009. An improved method for identifying red lakes on art and historical artifacts. Proceedings of the National Academy of Sciences 106, 36 (2009), 15095–15096. DOI:https://doi.org/10.1073/pnas.0907727106
[17]
I. Berezhnoy, E. Postma, and J. van den Herik. 2007. Computer analysis of van Gogh's complementary colours. Pattern Recognition Letters 28, 6 (2007), 703–709. DOI:https://doi.org/10.1016/j.patrec.2006.08.002
[18]
V. Dumoulin, J. Shlens, and M. Kudlur. 2017. A learned representation for artistic style. International Conference of Learned Representations (ICLR'17), Toulon, 1--26. DOI:
[19]
L. Shamir, T. Macura, N. Orlov, D. M. Eckley, and I. G. Goldberg. 2010. Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art. ACM Transactions on Applied Perception 7, 2 (2010), 1–17. DOI:https://doi.org/10.1145/1670671.1670672
[20]
K. C. Luber and A. Dürer. 2005. Albrecht Dürer And The Venetian Renaissance (1st Ed.). Cambridge University Press, Cambridge, 77--126.
[21]
K. Graddy. 2013. Taste endures! The rankings of Roger de Piles († 1709) and three centuries of art prices. The Journal of Economic History 73, 3 (2013), 766–791. DOI:https://doi.org/10.1017/S0022050 713000600
[22]
M. Brenson. 1986. Gallery view: Delacroix and Ingres continue their duel of fire and ice. New York Times 2, 27 (1986). Retrieved 11 August, 2022 from https://www.nytimes.com/1986/05/25/arts/gallery-view-delacroix-and-ingres-continue-their-duel-of-fire-and-ice.html.
[23]
R. K. Chaudhary. 2019. Influence of colour on visual arts. International Journal of Business Marketing and Management 4, 1 (2019), 4–9. Retrieved from http://www.ijbmm.com/vol5-issue1.html.
[24]
M. Yelizaveta, C. Tat-Seng, and A. Irina. 2005. Analysis and retrieval of paintings using artistic color concepts. In Proceedings of the 2005 IEEE International Conference on Multimedia and Expo. IEEE, Amsterdam, 1246--1249. DOI:https://doi.org/10.1109/ICME.2005.1521654
[25]
S. Burton. 2021. Mapping impressionist painting in transnational contexts (1st. ed). Transplanting Impressionism to Canada, Routledge, New York, NY, 65--76.
[26]
P. Hook. 2012. The Ultimate Trophy: How the Impressionist Painting Conquered the World. Prestel Publishing, New York, NY. 30--147.
[27]
M. Jacomy, T. Venturini, S. Heymann, and M. Bastian. 2014. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PloS One 9, 6 (2014), 1–12. DOI:https://doi.org/10.1371/journal.pone.0098679
[28]
Y. Yustiawan, W. Maharani, and A. A. Gozali. 2015. Degree centrality for social network with opsahl method. International Conference on Computer Science and Computational Intelligence (ICCSCI'15), Vol. 59, Procedia Computer Science, 419--426. DOI:
[29]
T. Weststeijn. 2012. The gender of colours in dutch art theory. Netherlands Yearbook for History of Art, Golden Age 62, 1 (2012), 176–201. DOI:https://doi.org/10.1163/22145966-06201008
[30]
G. Srikantan, D. Lee, and J. T. Favata. 1995. Comparison of normalization methods for character recognition. In Proceedings of the 3rd International Conference on Document Analysis and Recognition. IEEE, Montreal, QC, 719–722. DOI:https://doi.org/10.1109/ICDAR.1995.602004
[31]
Z. Hou and J. M. Parker. 2005. Texture defect detection using support vector machines with adaptive Gabor wavelet features. In Proceedings of the 2005 7th IEEE Workshops on Applications of Computer Vision. IEEE, Breckenridge, CO, 275–280. DOI:https://doi.org/10.1109/ACVMOT.2005.115
[32]
T. Qiao, W. Zhang, M. Zhang, Z. Ma, and D. Xu. 2019. Ancient painting to natural image: A new solution for painting processing. In Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision. IEEE, Waikoloa, HI, 521–530. DOI:https://doi.org/10.1109/WACV.2019.00061
[33]
A. Koschan and M. A. Abidi. 2008. Digital Colour Image Processing. Wiley, New Jersey, 53--64.
[34]
T. Putri, R. Mukundan, and K. Neshatian. 2018. Circular filtering and neutrosophic extraction of Vincent van Gogh's visible brushstrokes. In Proceedings of the IEEE Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE, Auckland, 1–6. https://doi.org/10.1109/IVCNZ.2018.8634702
[35]
H. Xu, J. Yan, N. Persson, W. Lin, and H. Zha. 2017. Fractal dimension invariant filtering and its CNN-based implementation. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Honolulu, HI, 3825–3833. DOI:https://doi.org/10.1109/CVPR.2017.407
[36]
Y. Mei, Y. Zhou, B. Zhao, and S. Chen. 2014. Orthogonal curved-line Gabor filter for fast fingerprint enhancement. Electronics Letters 50, 3 (2014), 175–177. DOI:https://doi.org/10.1049/el.2013.2619
[37]
M. Kim. 2021. Visualization of dynamic network evolution with quantification of node attributes. IEEE Transactions on Network Science and Engineering 8, 3 (2021), 2316–2325. DOI:https://doi.org/10.1109/TNSE.2021.3087334
[38]
T. Richard, A. P. Micolich, and D. Jonas. 1999. Fractal expressionism. Physics World 12, 10 (1999), 1–8. DOI:https://doi.org/10.1088/2058-7058/12/10/21
[39]
C. Brown and L. Liebovitch. 2010. Fractal Analysis (1st. ed.). Sage Publications, Inc., Los Angeles, 40--55.
[40]
J. C. Russ. 2013. Fractal Surfaces (1st. ed.). Plenum Press, New York, 4--5.
[41]
C. John and A. Karlqvist. 2003. Art and Complexity (1st. ed.). Elsevier, Amsterdam, 117--144.
[42]
H. Guosheng. 2019. Affective quantification of colour gestalt: modeling of affective factors for combinative color design. Leonardo 52, 2 (2019), 157–163. DOI:https://doi.org/10.1162/leon_a_01197
[43]
R. P. Kinsley and J. Portenoy. 2015. Perspectives of emerging museum professionals on the role of big data in museum. In Proceedings of the 2015 48th Hawaii International Conference on System Sciences. IEEE, Kauai, HI, 2075–2084. DOI:https://doi.org/10.1109/HICSS.2015.249
[44]
H. Margaret and L. K. John. 2003. On the LAM: Library, archive, and museum collections in the creation and maintenance of knowledge communities. Mapping Innovation: Six Depth Studies. Organization for Economic Co-operation and Development, Paris, Retrieved from http://www.oecd.org/dataoecd/59/63/32126054.pdf.
[45]
L. Dickerman and M. Affron. 2012. Inventing abstraction, 1910-1925: How a radical idea changed modern art. The Museum of Modern Art, New York. Retrieved 11 August, 2022 from https://www.moma.org/calendar/exhibitions/1273.
[46]
R. Lee, Y. Sohn, and W. Lee. 2017. Structuralizing the fluxus way of life: The social network of fluxus. Leonardo 50, 1 (2017), 74–75. DOI:https://doi.org/10.1162/LEON_a_01351
[47]
L. Avila and M. Bailey. 2016. Art in the digital age. IEEE Computer Graphics and Applications 36, 4 (2016), 6–7. DOI:https://doi.org/10.1109/MCG.2016.77
[48]
Microsoft and ING. 2016. The next rembrandt. Retrieved 11 August, 2022 from https://www.nextrembrandt.com/.
[49]
M. Van Welie. 2001. Task-based user interface. Dissertation Series of the Dutch Graduate School for Information and Knowledge Systems. Retrieved 11 August, 2022 from https://research.vu.nl/en/publications/task-based-user-interface-design.
[50]
A. N. Fahad, Z. Hassan, R. B. Sulaiman, and Z. Rahman. 2015. Usability evaluation of e-learning systems in the Iraqi higher education institutions. International Journal of Internet of Things 4, 1 (2015), 30–34. DOI:https://doi.org/10.5923/c.ijit.201501.05
[51]
J. R. Lewis. 1992. Psychometric evaluation of the post-study system usability questionnaire: The PSSUQ. Proceedings of the Human Factors Society Annual Meeting 36, 16 (1992), 1259–1260. DOI:https://doi.org/10.1177/154193129203601617
[52]
J. R. Lewis. 2002. Psychometric evaluation of the PSSUQ using data from five years of usability studies. International Journal of Human-Computer Interaction 14, 3–4 (2002), 463–488. DOI:https://doi.org/10.1080/10447318.2002.9669130
[53]
A. Fruhling and S. Lee. 2005. Assessing the reliability, validity and adaptability of PSSUQ. In Proceedings of the Americas Conference on Imformation System (AMCIS'05). Vol. 378, 2394–2402. http://aisel.aisnet.org/amcis2005/378.
[54]
R. Kumar and N. Hasteer. 2017. Evaluating usability of a web application: A comparative analysis of open-source tools. In Proceedings of the 2017 2nd International Conference on Communication and Electronics Systems. IEEE, Coimbatore, 350–354. DOI:https://doi.org/10.1109/CESYS.2017.8321296
[55]
B. Battleson, A. Booth, and J. Weintrop. 2001. Usability testing of an academic library web site: A case study. Journal of Academic Librarianship 27, 3 (2001), 188–198. DOI:https://doi.org/10.1016/S0099-1333(01)00180-X
[56]
R. Dickstein and V. Mills. 2000. Usability testing at the university of arizona library: How to let the users in on the design. Information Technology and Libraries 19, 3 (2000), 144–151. Retrieved from https://www.proquest.com/scholarly-journals/usability-testing-at-university-arizona-library/docview/215830070/se-2.
[57]
W. Hwang and G. Salvendy. 2010. Number of people required for usability evaluation: the 10±2 rule. Communications of the ACM 53, 5 (2010), 130–133. DOI:https://doi.org/10.1145/1735223.1735255
[58]
R. Alroobaea and P. J. Mayhew. 2014. How many participants are really enough for usability studies?. In Proceedings of the 2014 Science and Information Conference. IEEE, London, 48–56. DOI:https://doi.org/10.1109/SAI.2014.6918171
[59]
K. Caine. 2016. Local standards for sample size at CHI. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, California, 981–992. DOI:https://doi.org/10.1145/2858036.2858498
[60]
A. Cazañas, A. de San Miguel, and E. Parra. 2017. Estimating sample size for usability testing. Enfoque UTE 8, 1 (2017), 172–185. DOI:https://doi.org/10.29019/enfoqueute.v8n1.126
[61]
T. Tran, P. Haase, H. Lewen, Ó. Muñoz-García, A. Gómez-Pérez, and R. Studer. 2007. Lifecycle-support in architectures for ontology-based information systems. Lecture Notes in Computer Science Book Series, Vol. 4825, Springer, 508--522. DOI:
[62]
J. A. Khan, D. Sangroha, M. Ahmad, and M. T. Rahman. 2014. A performance evaluation of semantic based search engines and keyword based search engines. In Proceedings of the 2014 International Conference on Medical Imaging, m-Health and Emerging Communication. IEEE, Greater Noida, 168–173. DOI:https://doi.org/10.1109/MedCom.2014.7005997
[63]
J. S. Lee, S. C. Park, and H. H. Hahm. 2015. Dynamic and efficient search system for digital encyclopedia of intangible cultural heritage: The case study of ICHPEDIA. In Proceedings of the Editors Ubiquitous Computing Application and Wireless Sensor. Vol. 331, Springer, 679–685. DOI:https://doi.org/10.1007/978-94-017-9618-7_72

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Published In

cover image Journal on Computing and Cultural Heritage
Journal on Computing and Cultural Heritage   Volume 15, Issue 4
December 2022
483 pages
ISSN:1556-4673
EISSN:1556-4711
DOI:10.1145/3572828
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 December 2022
Online AM: 26 July 2022
Accepted: 10 April 2022
Received: 31 August 2021
Published in JOCCH Volume 15, Issue 4

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Author Tags

  1. Artistic stylization network
  2. artwork
  3. image processing
  4. quantitative analysis
  5. visual stylometry
  6. signature style
  7. semantic-based search engine

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  • Refereed

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  • Ministry of Education of the Republic of Korea and the National Research Foundation of Korea

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