CNN Classification of the Cultural Heritage Images

Abstract

The cultural heritage image classification represents one of the most important tasks in the process of digitalization. In this paper, a deep learning neural network was applied in order to classify images of architectural heritage belonging to ten categories, in particular: (i) bell tower, (ii) stained glass, (iii) vault, (iv) column, (v) outer dome, (vi) altar, (vii) apse, (viii) inner dome, (ix) flying buttress, and (x) gargoyle. The Convolutional neural network was used for image classification, with the same architecture applied on two sets of the data: the full dataset consisting of 10 categories as well as dataset with 5 different image categories. The results show that both architectures performed well and obtained accuracy of up to 90%.