Object Classification in Images of Neoclassical Artifacts Using Deep Learning
This provides scholars with new methods for analyzing Neoclassical artifacts, but it is incremental as it applies existing deep learning techniques to a new domain.
The paper tackles the problem of classifying artifacts in images of Neoclassical art using deep learning, reporting a high accuracy for locating and classifying multiple objects in images.
In this paper, we report on our efforts for using Deep Learning for classifying artifacts and their features in digital visuals as a part of the Neoclassica framework. It was conceived to provide scholars with new methods for analyzing and classifying artifacts and aesthetic forms from the era of Classicism. The framework accommodates both traditional knowledge representation as a formal ontology and data-driven knowledge discovery, where cultural patterns will be identified by means of algorithms in statistical analysis and machine learning. We created a Deep Learning approach trained on photographs to classify the objects inside these photographs. In a next step, we will apply a different Deep Learning approach. It is capable of locating multiple objects inside an image and classifying them with a high accuracy.