Grey level co-occurrence matrix and learning algorithms to quantify and classify use-wear on experimental flint tools

Today, the analysis of use-wear traces of archaeological artefacts uses both qualitative (traditional) and quan-titative (innovative) techniques. However, quantitative analyses require time and huge economic resources. It is important to find quantitative solutions that do not have high costs and that allow qualitative analysis to be more robust. Furthermore, quantitative analysis with 2D techniques would facilitate the standardization of the terms used to discriminate and describe the different types of polish.The Gray-Level Co-occurrence Matrix is a 2D image analysis technique that provides quantitative data on image texture. Although quite common in other research field, this technique has not been systematically applied in the archaeological research. Unlike other quantitative approaches this technique takes little time and has no cost, as it uses traditional 2D images. Futhermore, this method acts as a bridge between qualitative and quan-titative analyses. In this first approach, four use-wear traces (unused, butchering, shell, wood, antler) on flint experimental samples were analysed with selections of square images of 300x300 and 600x600 pixels (30 for each contact material).This technique together with a learning algorithm called Support Vector Machine has proved effective in distinguishing and quantifying four types of use-wear traces.