Abstract
Chinese painting is famous and valuable for special painting materials, skills and final art effects used, yet this has resulted in many fake paintings being produced. Those fake paintings were normally made by using modern high resolution scanning and printing technology, thus it is very hard to identify the fake ones by human vision. To address this challenging problem, in this paper, a hyperspectral image based features fusion method is proposed. Firstly, we scan Chinese paintings using a visual band hyperspectral camera with the spectral frequency ranging from 400 to 900 nm. Then, the spectral and spatial features are extracted respectively by using the principal component analysis and a convolution neural network. Finally, we fuse these two kinds of features and input the feature set into a support vector machines for classification. All samples of real and fake paintings are obtained from local Chinese painting organization. The experimental result shows the effectiveness of the proposed method with an accuracy achieved of 84.6 %, which is significantly higher than other approaches where only spectral or spatial feature is used.
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Wang, Z., Lu, D., Zhang, D. et al. Fake modern Chinese painting identification based on spectral–spatial feature fusion on hyperspectral image. Multidim Syst Sign Process 27, 1031–1044 (2016). https://doi.org/10.1007/s11045-016-0429-9
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DOI: https://doi.org/10.1007/s11045-016-0429-9