Cultural assets identification using transfer learning

Abstract

Identifying cultural assets is a challenging task which requires specific expertise. In this paper, a deep learning based solution to identify archaeological objects is proposed. Several additions to the ResNet CNN architecture are introduced which consolidate features from different intermediate layers by applying global pooling operations. Unlike general object recognition, identifying archaeological objects poses new challenges. To meet the special requirements in classifying antiques, a hybrid network architecture is used to learn the characteristics of objects using transfer learning, which includes a classification network and a regression network. With the help of the regression network, the age of objects can be predicted, which improves the overall performance in comparison to manually classifying the age of objects. The proposed scheme is evaluated using a public database of cultural assets and the experimental results demonstrate its significant performance in identifying antique objects.