The aim of this position paper is to establish an initial approach to the automatic classification of digital images about the Outsider Art style of painting. Specifically, we explore whether is it possible to classify non-traditional artistic styles by using the same features that are used for classifying traditional styles? Our research question is motivated by two facts. First, art historians state that non-traditional styles are influenced by factors “outside” of the world of art. Second, some studies have shown that several artistic styles confound certain classification techniques. Following current approaches to style prediction, this paper utilises Deep Learning methods to encode image features. Our preliminary experiments have provided motivation to think that, as is the case with traditional styles, Outsider Art can be computationally modelled with objective means by using training datasets and CNN models. Nevertheless, our results are not conclusive due to the lack of a large available dataset on Outsider Art. Therefore, at the end of the paper, we have mapped future lines of action, which include the compilation of a large dataset of Outsider Art images and the creation of an ontology of Outsider Art.

The purpose of this paper is to present a prospective and interdisciplinary research project seeking to ontologize knowledge of the domain of Outsider Art, that is, the art created outside the boundaries of official culture. The goal is to combine ontology engineering methodologies to develop a knowledge base which i) examines the relation between social exclusion and cultural productions, ii) standardizes the terminology of Outsider Art and iii) enables semantic interoperability between cultural metadata relevant to Outsider Art. The Outsider Art ontology will integrate some existing ontologies and terminologies, such as the CIDOC - Conceptual Reference Model (CRM), the Art & Architecture Thesaurus and the Getty Union List of Artist Names, among other resources. Natural Language Processing and Machine Learning techniques will be fundamental instruments for knowledge acquisition and elicitation. NLP techniques will be used to annotate bibliographies of relevant outsider artists and descriptions of outsider artworks with linguistic information. Machine Learning techniques will be leveraged to acquire knowledge from linguistic features embedded in both types of texts.