Feature Relevance in Classification of 3D Stone from Ancient Wall Structures

The increasing availability of quantitative data in archaeological studies has prompted the research of Machine Learning methods to support archaeologists in their analysis. This paper considers in particular the problem of automatic classification of 3D surface patches of “rubble stones” and “wedges” obtained from Prehistorical and Protohistorical walls in Crete. These data come from the W.A.L.(L) Project aimed to query 3D photogrammetric models of ancient architectonical structures in order to extract archaeologically significant features. The principal aim of this paper is to address the issue of a clear semantically correspondence between data analysis concepts and archaeology. Classification of stone patches has been performed with several Machine Learning methods, and then feature relevance has been computed for all the classifiers. The results show a good correspondence between the most relevant features of the classification and the qualitative features that human experts adopt typically to classify the wall facing stones.