A Content-Based Knowledge and Data Intensive System for Archaeological Motif Recognition

Archaeological excavations typically turn up large amounts of fragments from different ceramic objects. To preserve fragile historical remains, archaeologists generally rely on digital imaging techniques which are time consuming and require special technical training. Previous attempts to devise ways of facilitating this process have concentrated on the automatic assessment and recording of the shape features of potter forms to allow for automated reconstruction. In this paper, we present a content-based knowledge and data intensive system for archaeological motif recognition. It automatically recognizes motifs on fragments, thus facilitating human experts in reconstructing archaeological artifacts and in managing knowledge related to the decorative motif. The system consists of three modules: fragment images preprocessing, motif encoding, and element detection and template matching. The fragment preprocessing module reduces noise and provides a user interface to define region of interest (ROI). The motif encoding module then introduces the Lapita Pottery Online Database with element definitions. Finally, in the element detection and template matching module, detected elements are labeled and the related motif is recommended. Experimental results indicate that the proposed method not only increases scalability, but also contributes to the knowledge management and discovery of reconstructed archaeological relics.