TY - JOUR
T1 - Curvature-based authentication of van Gogh paintings
AU - Liu , Haixia
AU - Tai, Xue-Cheng
N1 - The authors also would like to thank the Isaac Newton Institute for Mathematical Sciences for its hospitality during the programme ‘variational methods and effective algorithms for imaging and vision’ which was support by EPSRC Grant Number EP/K032208/1. Haixia Liu would like to thank Norwegian Research Council project through ISP-Matematikk (Project no. 239033/F20) to support her stay.
PY - 2020/4/2
Y1 - 2020/4/2
N2 - Art authentication is the identification of genuine paintings by famous artists from the forgeries. In this paper, we introduce a novel curvature-based method to authenticate van Gogh paintings. We use curvature images to capture the shape information in the paintings. For each painting, we convert it from RGB to HSI color space. The features we propose are two simple statistics of the three parts: including (i) the H, S, I color information, (ii) their corresponding first order derivatives in x,y directions, and (iii) the corresponding 2D curvature images. In order to select the appropriate features for art authentication, we use a forward stage-wise feature selection method such that van Gogh paintings are highly concentrated and forgeries are spread around as outliers. Numerical results show that our method gives the 88.61% classification accuracy, which outperforms the state-of-the-art methods for art authentication so far.
AB - Art authentication is the identification of genuine paintings by famous artists from the forgeries. In this paper, we introduce a novel curvature-based method to authenticate van Gogh paintings. We use curvature images to capture the shape information in the paintings. For each painting, we convert it from RGB to HSI color space. The features we propose are two simple statistics of the three parts: including (i) the H, S, I color information, (ii) their corresponding first order derivatives in x,y directions, and (iii) the corresponding 2D curvature images. In order to select the appropriate features for art authentication, we use a forward stage-wise feature selection method such that van Gogh paintings are highly concentrated and forgeries are spread around as outliers. Numerical results show that our method gives the 88.61% classification accuracy, which outperforms the state-of-the-art methods for art authentication so far.
U2 - 10.4310/MAA.2019.v26.n3.a4
DO - 10.4310/MAA.2019.v26.n3.a4
M3 - Journal article
SN - 1073-2772
VL - 26
SP - 269
EP - 280
JO - Methods and Applications of Analysis
JF - Methods and Applications of Analysis
IS - 3
ER -