Authentication of Art: Assessing the Performance of a Machine Learning Based Authentication Method-Reference-Cited by-同舟云学术

Authentication of Art: Assessing the Performance of a Machine Learning Based Authentication Method

Published:2020 Issue: Volume: Page:328-342
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Author:

Chen Ailin,Jesus Rui,Vilarigues Márcia

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-53294-9_22

Reference12 articles.

1. Montagner, C.: The brushstroke and materials of Amadeo de Souza-Cardoso combined in an authentication tool. Ph.D Dissertation, Departamento de Conservação e Restauro, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa (2015)

2. Montagner, C., Jesus, R., Correia, N., Vilarigues, M., Macedo, R., Melo, M.J.: Features combination for art authentication studies: brushstroke and materials analysis of amadeo de souza-cardoso. Multimedia Tools Appl. 75(7), 4039–4063 (2016). https://doi.org/10.1007/s11042-015-3197-x

3. Keren, D.: Painter identification using local features and naive bayes. In: Proceedings of the 16th international conference on pattern recognition, vol. 2, pp. 474–477. IEEE (2002)

4. Li, J., Wang, J.Z.: Studying digital imagery of ancient paintings by mixtures of stochastic models. IEEE Trans. Image Process. 13(3), 340–353 (2003)

5. Lombardi, T.: The classification of style in fine-art painting. Dissertation, Pace University (2005)

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1. Extended Realities, Artificial Intelligence and Interfaces;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2020