Skip to main content
SpringerLink
Log in

Features combination for art authentication studies: brushstroke and materials analysis of Amadeo de Souza-Cardoso

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This work presents a tool to support authentication studies of paintings attributed to the modernist Portuguese artist Amadeo de Souza-Cardoso (1887-1918). The strategy adopted was to quantify and combine the information extracted from the analysis of the brushstroke with information on the pigments present in the paintings. The brushstroke analysis was performed combining Gabor filter and Scale Invariant Feature Transform. Hyperspectral imaging and elemental analysis were used to compare the materials in the painting with those present in a database of oil paint tubes used by the artist. The outputs of the tool are a quantitative indicator for authenticity, and a mapping image that indicates the areas where materials not coherent with Amadeo’s palette were detected, if any. This output is a simple and effective way of assessing the results of the system. The method was tested in twelve paintings obtaining promising results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. In the database of reference materials was included: cobalt violet; vermillion; carmine lake; terra rossa; raw Siena; ochre yellow; chrome yellow; cadmium orange; cobalt blue; cerulean blue; Prussian blue; ultramarine blue; viridian; emerald green; lead white; ivory black.

  2. The selection of the points for the elemental analysis is preceded by the observation of the painting by naked eye and by microscope. Some areas may not be included because they are considered consistent with other already selected areas.

References

  1. Al-Ayyoub M, Irfan MT, Stork DG (2011) Computer vision and Image Analysis of Art II. 7869

  2. Alfaro C (2008) Critérios e metodologia, in Catálogo Raisonné V.2 – Amadeo de Souza-Cardoso, pintura, A.A.a.F.C. Gulbenkian, Editor. Lisbon

  3. Almeida P (2012) Análise e Identificação de Obras de Arte. Universidade NOVA de Lisboa, Faculdade De Ciências e Tecnologia, Portugal Lisbon

  4. Almeida P et al (2013) Analysis of paintings using multi-sensor data, in Signal Processing Conference (EUSIPCO), IEEE, Editor. Marrakech. p. 1-5

  5. Barni M, Pelagotti A, Piva A (2005) Image processing for the analysis and conservation of paintings: opportunities and challenges. Signal Proc Mag IEEE 22(5):141–144

    Article  Google Scholar 

  6. Baronti S et al (1997) Principal Component Analysis of visible and near-infrared multispectral images of work of art. Chemom Intell Lab Syst 39(1):103–144

    Article  Google Scholar 

  7. Berezhnoy IE, Postma EO, Herik van den HJ (2005) Computerized visual analysis of paintings. in Humanities, computers and cultural heritage: Proceedings of the XVI international conference of the Association for History and Computing. Amsterdam: Amsterdam: Royal Netherlands Academy of Arts and Sciences

  8. Berezhnoy IE et al (2009) Automatic extraction of brushstroke orientation from paintings. POET: Prevailing Orientation Extraction Technique. Mach Vis Appl 20:1–9

    Article  Google Scholar 

  9. Boselli L, Picollo M, Radicati B (2011) UV Vis, NIR Fibre Optic Reflectance Spectroscopy (FORS) in Practical handbook on diagnosis of paintings on movable support, R.M. D. Pinna, B. Brunetti, Editor, Artech

  10. Browne MW (2000) Cross-Validation Methods. J Math Psychol 44(1):108–132

    Article  MathSciNet  MATH  Google Scholar 

  11. Cesaratto A et al (2013) A novel classification method for multispectral imaging combined with portable Raman spectroscopy for the analysis of a painting by Vincent van Gogh. Appl Spectroscopy 67(11):1234–41

    Article  Google Scholar 

  12. Comelli D et al (2011) Insights into Masolino’s wall paintings in Castiglione Olona: advanced reflectance and fluorescence imaging analysis. J Cultural Heritage 12(1):11–18

    Article  Google Scholar 

  13. Cornelis B et al (2009) Report on Digital Image Processing for Art Historians, in International Conference on Sampling Theory and Applications. Marseille, France

  14. Deborah H, George S, Hardeberg JY (2014) Pigment mapping of the Scream (1893) based on hyperspectral imaging. in ICIP. Cherbourg, France Springer International Publishing.

  15. Delaney JK et al (2010) Visible and infrared imaging spectroscopy of Picasso’s Harlequin musician: mapping and identification of artist materials in situ. Appl Spectrosc 64(6):584–594

    Article  Google Scholar 

  16. Delaney JK et al (2014) Use of imaging spectroscopy, Fiber Optic Reflectance Spectroscopy, and X-ray fluorescence to map and identify pigments in illuminated manuscripts. Studies In Conservation 59(2):91–101

    Article  Google Scholar 

  17. Doulamis AD, Varvarigou TA (2011) Image analysis for artistic style identification: A powerful tool for preserving Cultural Heritage. in 5th Conference on Emerging Technologies in Non-Destructive Testing V

  18. Erhan D et al (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660

    MathSciNet  MATH  Google Scholar 

  19. Feller RJ (2004) Color science in the examination of museum objects: non-destructive procedures. The Getty Conservation Institute, Los Angeles

    Google Scholar 

  20. França JA, Amadeo de Souza-Cardoso (1956) Lisbona:Sul.Lisbon

  21. Freitas H, Alfaro C (2008) Catálogo Raisonné V.2 – Amadeo de Souza-Cardoso, pintura, ed. A.A.a.F.C. Gulbenkian, Lisbon

  22. Freitas H, Alfaro C, Rosa M (2006) Diálogo de Vanguardas-Avant-Garde Dialogues. Lisbon: Assírio & Alvim and Fundação Calouste Gulbenkian.

  23. Gabor D (1946) Theory of communication. J Ins Electric Eng Part III: Radio Commun Eng 93(26):429–441

    Google Scholar 

  24. Hendriks E, Hughes S (2009) van Gogh’s brushstrokes: marks of authenticity? in Art, conservation and authenticities: material, concept, context international conference, Archetype, Editor. London. p. 143-154

  25. Hendriks E, Tilborgh van L (2006) New views on Van Gogh’s development in Antwerp and Paris: An integrated art historical and technical study of his paintings in the Van Gogh Museum, in Faculty of Humanities. Univ. Amsterdam

  26. Jesus R (2009) Recuperação de informação multimédia em memórias pessoais, in Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia, Portugal, Lisbon.

  27. Jesus R, Abrantes A, Correia N (2010) Methods for automatic and assisted image annotation. Multimedia Tools and Applications, p. 1-20

  28. Johnson CR et al (2008) Image processing for artist identification computerized analysis of Vincent van Gogh’s painting brushstrokes. IEEE Signal Process Mag 25(4):37–48

    Article  Google Scholar 

  29. Keshava N (2004) Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Trans Geosci Remote Sens 42(7):1552–1565

    Article  Google Scholar 

  30. Keshava N, Mustard JF (2002) Spectral Unmixing. IEEE Signal Process Mag 19(1):44–57

    Article  Google Scholar 

  31. Kumar A, Daumé H III (2011) A Co-training approach for multi-view spectral clustering. in Proceedings of the 28th International Conference on Machine Learning (ICML 2011). Bellevue, Washington, USA.

  32. Lew MS et al (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19

    Article  MathSciNet  Google Scholar 

  33. Li J et al (2012) Rhythmic brushstrokes distinguish van Gogh from his contemporaries: findings via automated brushstroke extraction Pattern Analysis and Machine Intelligence. IEEE Trans 34(6):1159–1176

    Google Scholar 

  34. Liang H (2012) Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Applied Physic A 106:309–323

    Article  Google Scholar 

  35. Linhares JMM, Pinto PD, Nascimento SMC (2008) The number of discernible colors in natural scenes. J Optic Soc Am Optics Image Scie Vision 25(12):2918–2924

    Article  Google Scholar 

  36. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, p. 91-110

  37. Lowe DG (1999) Object recognition from local scale-invariant features, in International Conference on Computer Vision. Corfu, Greece. p. 1150-1157

  38. Maarten van der L, Postma E (2008) Digital analysis of van Gogh paintings, in AAAI Conference on Artificial intelligence

  39. Makantasis K et al (2015) Deep supervised learning for hyperspectral data classification through convolutional neural networks, in Geoscience and Remote Sensing Symposium (IGARSS 2015).

  40. Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8): p. 837-842

  41. Melo MJ et al (2008) Uma mão cheia de cores, O Século XX e o nascimento da arte moderna, in Catálogo Raisonné V.2 – Amadeo de Souza-Cardoso, pintura, A.A.a.F.C. Gulbenkian, Editor. Lisbon.

  42. Montagner C (2015) The brushstroke and materials of Amadeo de Souza-Cardoso combined in an authentication tool, in Department of Conservation and Restoration, Faculdade de Ciências e Tecnologia - Universidade Nova de Lisboa: Lisbon.

  43. Montagner C et al (2012) Unveiling the hand of a 19th Century artist with binary image classification and Bag-of-Features., in 19th International conference on Systems, Signals and Image (IWSSIP) IEEE: Austria. p. 201-204.

  44. Montagner C et al (2013) Behind the surface - Hyperspectral image spectroscopy for artist authentication, in 12th International AIC Congress, AIC, Editor. p. 359-362

  45. Nowak E, Jurie F, Triggs B (2006) Sampling strategies for Bag-of Features image classification, in European Conference on Computer Vision. Graz, Austria. p. 3954:490-503

  46. Pelagotti A et al (2008) Multispectral imaging of paintings, a way to material identification. IEEE Signal Process Mag 36:27–36

    Article  Google Scholar 

  47. Pinto DP, Linhares JMM, Nascimento SMC (2008) Correlated color temperature preferred by observers for illumination of artistic paintings. J Opt Soc Am A 25:623–630

    Article  Google Scholar 

  48. Poggio T, Smale S (2003) The mathematics of learning: dealing with data. Notice Am Math Soc 50(5):537–544

    MathSciNet  MATH  Google Scholar 

  49. Robertson S (2004) Understanding Inverse Document Frequency: on theoretical arguments for IDF. J Doc 60(5):503–520

    Article  Google Scholar 

  50. Saleh B et al (2014) Toward automated discovery of artistic influence Multimedia Tools and Applications, p. 1-17

  51. Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639

    Article  Google Scholar 

  52. Shen J (2009) Stochastic modelling western paintings for effective classification. Pattern Recogn 42:293–301

    Article  MATH  Google Scholar 

  53. Stork DG (2006) Computer Vision, Image Analysis, and Master Art: Part 1. IEEE MultiMedia 13(3):16–20

    Article  MathSciNet  Google Scholar 

  54. Stork DG, Duarte MF (2006) Computer Vision, Image Analysis, and Master Art: Part 3. MultiMedia, IEEE 14(1):14–18

    Article  Google Scholar 

  55. Stork DG, Johnson MK (2006) Computer Vision, Image Analysis, and Master Art: Part 2. MultiMedia, IEEE 13(4):12–17

    Article  Google Scholar 

  56. Taylor RP, Micolich AP, Jonas D (1999) Fractal analysis of Pollock drip paintings. Nature, 399(422).

  57. Zhao Y (2008) Image segmentation and pigment mapping of cultural heritage based on spectral imaging, in PhD. Rochester, New York, United States

Download references

Acknowledgments

This work has been supported by national funds through FCT- Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) under project PTDC/EAT-EAT/113612/2009 and grant of Cristina Montagner SFRH/BD/66488/2009 as well as REQUIMTE supporting project PEst-C/EQB/LA0006/2011. The authors are grateful to all team members of CAM - Centro de Arte Moderna da Fundação Gulbenkian for the fruitful collaboration, in particular to director Isabel Carlos and curator Ana Vasconcelos e Melo. Thanks also to Professor Sérgio Nascimento, João M.M. Linhares, Osamu Masuda and Hélder Tiago Correia for the spectral imaging analysis.

Author information

Authors and Affiliations

  1. Requimte, LAQV, and Departamento de Conservação e Restauro, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Monte de Caparica, 2829-516, Portugal

    Cristina Montagner & Maria João Melo

  2. VICARTE and Departamento de Conservação e Restauro, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Monte de Caparica, 2829-516, Portugal

    Márcia Vilarigues

  3. IHA and Departamento de Conservação e Restauro, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Monte de Caparica, 2829-516, Portugal

    Rita Macedo

  4. Multimedia and Machine Learning Group, Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emídio Navarro n°1, Lisbon, Portugal

    Rui Jesus

  5. NOVA-LINCS, Departamento de Informática, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Lisbon, Portugal

    Nuno Correia

Authors
  1. Cristina Montagner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Jesus
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nuno Correia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Márcia Vilarigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rita Macedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maria João Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Montagner.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Montagner, C., Jesus, R., Correia, N. et al. Features combination for art authentication studies: brushstroke and materials analysis of Amadeo de Souza-Cardoso. Multimed Tools Appl 75, 4039–4063 (2016). https://doi.org/10.1007/s11042-015-3197-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-3197-x

Keywords

Search

Navigation