Archaeological Fragments Classification Based on Rgb Color and Texture Features

Artifacts are often found in archaeological excavation sites mixed with each other randomly. Therefore, classifying them manually is a difficult task and time consuming because they commonly exceed thousands of fragments. Thus, the aim of this study is to find a solution for classification of ancient pottery into groups by computer assistance. This is a preparatory stage for the next phase, which is the reconstruction of the archaeological fragments with high accuracy. To solve this problem, several steps must be taken, which are image segmentation via a proposed algorithm, and cluster the fragments into groups based on color and texture features. We proposed a novel algorithm that relies on the intersection of the RGB color between the archaeological fragments, and extraction of texture features from fragments based on Gray Level Cooccurrence Matrix (GLCM) that include Energy, Contrast, Correlation and Homogeneity. Finally, by using both proposed algorithm for classifying the color feature, and Euclidean distance for classifying the texture feature, we can classify the fragments with a high accuracy. The algorithm was tested on a pottery database, and it achieved a success rate almost 95%, so we would like to point out that by using the proposed algorithms we achieved promising results.