@article{article_782936, title={Classification of Historic Ornaments with CNN: Issues for Interdisciplinary Studies}, journal={Journal of Computational Design}, volume={1}, pages={115–130}, year={2020}, author={Altun, Sevgi and Güneş, Mustafa Cem}, keywords={convolutional neural networks, architectural heritage, architectural ornament, anatolian seljuk ornaments, artificial intelligence}, abstract={This paper is a critical assessment of an exploration of computer vision and deep learning methods in an architectural heritage context. Convolutional neural network, a type of deep learning is implemented to classify a group of Anatolian Seljuk ornamental patterns. The field of computer vision offers the potentials to assist studies in the field of architectural heritage. However, there are limited studies that combine knowledge across the two fields. One frequently studied topic is image classification based on features. In this study, we took on the task of classifying Anatolian Seljuk ornamental patterns to investigate the potential. The project focused on carved ornamental patterns on flat surfaces due to ease of data collection. The group of images is collected and arranged as two different yet related datasets. The classes are floral and geometrical, and subclasses are sparse and dense for both. Two different CNN architectures are used to train models for predictions. The process and effect of dataset creation on the implementation are explained. Results are discussed from both the technical and architectural points of view, providing a basis for further interdisciplinary studies.}, number={3}, publisher={İstanbul Technical University}