Object Classification in Images of Neoclassical Artifacts Using Deep Learning

TL;DR

This paper presents a deep learning framework for classifying artifacts in images of Neoclassical art, aiding scholars in analyzing cultural patterns through automated visual recognition.

Contribution

It introduces a deep learning method trained on photographs to classify objects in images of Neoclassical artifacts, enhancing traditional analysis with data-driven techniques.

Findings

High accuracy in object classification within artifact images

Framework supports both knowledge representation and discovery

Potential for identifying cultural patterns in art images

Abstract

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.