Abstract
This paper is about authenticating genuine van Gogh paintings from forgeries. The paintings used in the test in this paper are provided by van Gogh Museum and Kröller-Müller Museum. The authentication process depends on two key steps: feature extraction and outlier detection. In this paper, a geometric tight frame and some simple statistics of the tight frame coefficients are used to extract features from the paintings. Then a forward stage-wise rank boosting is used to select a small set of features for more accurate classification so that van Gogh paintings are highly concentrated towards some center point while forgeries are spread out as outliers. Numerical results show that our method can achieve 86.08% classification accuracy under the leave-one-out cross-validation procedure. Our method also identifies five features that are much more predominant than other features. Using just these five features for classification, our method can give 88.61% classification accuracy which is the highest so far reported in literature. Evaluation of the five features is also performed on two hundred datasets generated by bootstrap sampling with replacement. The median and the mean are 88.61% and 87.77% respectively. Our results show that a small set of statistics of the tight frame coefficients along certain orientations can serve as discriminative features for van Gogh paintings. It is more important to look at the tail distributions of such directional coefficients than mean values and standard deviations. It reflects a highly consistent style in van Gogh's brushstroke movements, where many forgeries demonstrate a more diverse spread in these features.
Original language | English |
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Pages (from-to) | 590-602 |
Number of pages | 13 |
Journal | Applied and Computational Harmonic Analysis |
Volume | 41 |
Issue number | 2 |
Early online date | 2 Dec 2015 |
DOIs | |
Publication status | Published - Sept 2016 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2015 Elsevier Inc.
Funding
We thank Profs. Haixiang Lin and Eric Postma for their helpful discussions and providing us with the 79 paintings used in this paper. The dataset was used while being affiliated with the Tilburg center for Cognition and Communication. Research of Raymond H. Chan is supported in part by HKRGC GRF Grant No. CUHK400412 , HKRGC CRF Grant No. CUHK2/CRF/11G , HKRGC AoE Grant AoE/M-05/12 , CUHK DAG No. 4053007 , and CUHK FIS Grant No. 1902036 . The research of Yuan Yao is supported in part by National Basic Research Program of China (973 Program 2012CB825501 , 2015CB856000 ), NSFC grants 61071157 and 61370004 .
Keywords
- Art authentication
- Feature selection
- Outlier detection
- Stylometry
- Tight frame