Abstract
Ancient paintings are valuable for historians and archeologists to study the humanities, customs and economy of the corresponding eras. For this purpose, it is important to first determine the era in which a painting was drawn. This problem can be very challenging when the paintings from different eras present a same topic and only show subtle difference in terms of the painting styles. In this paper, we propose a novel computational approach to address this problem by using the appearance and shape features extracted from the paintings. In this approach, we first extract the appearance and shape features using the SIFT and kAS descriptors, respectively. We then encode these features with deep learning in an unsupervised way. Finally, we combine all the features in the form of bag-of-visual-words and train a classifier in a supervised fashion. In the experiments, we collect 660 Flying-Apsaras paintings from Mogao Grottoes in Dunhuang, China and classify them into three different eras, with very promising results.
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