Abstract
This chapter combines traditional artificial intelligence (AI) concepts, i.e., computational ontologies, with more recent trends, i.e., deep learning for content-based semantic retrieval in Cultural Heritage. More specifically, the proposed AI-empowered system employs computational ontologies for modelling photographs of religious historical buildings. The ontology, besides supporting data-modelling and concept-level annotation, guides a learning process – implemented through Convolutional Neural Network (CNN) – for automated image categorization and retrieval. The whole system has been tested on the ruins of the church of Santa Maria delle Grazie in Misterbianco, Catania, Italy, showing satisfactory performance.
TopBackground
The authors focus this literature review on two main topics: i) the use of ontologies for the classification of Cultural Heritage data and ii) some relevant Deep Learning techniques specifically for image retrieval and classification in the CH field.
Key Terms in this Chapter
Computational Ontology: mean to formally model the structure of a system, i.e., the relevant entities and relations that emerge from its observation, and which are useful to our purposes.
Convolutional Neural Network (CNN): is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.
Visual Programming Language (VPL): is a programming language that uses graphical elements and figures to develop a program. A VPL employs techniques to design a software program in two or more dimensions, and includes graphical elements, text, symbols and icons within its programming context.
Information Retrieval: is the tracing and recovery of specific information from stored data.
Deep Learning (DL): is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Learning can be supervised, semi-supervised or unsupervised.
Artificial Neural Network (NN): is an interconnected group of nodes, inspired by a simplification of neurons in a brain.