Cultural heritage represents a reliable medium for history and knowledge transfer. Cultural heritage assets are usually exhibited in museums and heritage sites all over the world. However, many assets are poorly labelled, which decreases their historical value. In fact, if an asset’s history is lost, its historical value is also lost. Classification and annotation of overlooked or incomplete cultural assets increase their historical value and allow discovering various types of historical links. The goal of this thesis is to to automatically classify and annotate cultural heritage assets using their visual features in addition to the metadata available at hand. In this context, the candidate will be required to study, investigate and apply machine-learning and deep learning approaches for image processing and classification.
Aim: Applying machine learning and deep learning for automatically classifying and annotating cultural heritage assets.
Supervisors: Prof. Emanuele Frontoni, Prof.ssa Eva Savina Malinverni, Roberto Pierdicca, Marina Paolanti, Andrea Felicetti
Start: Sept, 2018
Expected graduation: Feb/July, 2019
Skills that will be acquired: Programming (Python, MATLAB)
Contacts: {e.frontoni,m.paolanti}@univpm.it