A Greek Pottery Shape and School Identification and Classification System Using Image Retrieval Techniques

In this paper we propose an image-based pottery shape and school identification and classification system for an unknown pottery or fragment. This system is designed to assist archaeologists and students to identify and record objects quickly and accurately. We present several image retrieval and computer vision techniques and describe their applications within the domain of archaeological studies by utilizing a large digital library of pottery photos. Twenty pottery shapes and four pottery schools are identified with shape and color-based image retrieval techniques, respectively. The system analyses and compares extracted features to determine the five closest database images, and then presents them to the user for final decision. This is the first pottery study to combine the two different techniques – shape and color-based image retrieval – to identify multiple characteristics of an unknown pottery image or a pottery fragment. The database contains about one thousand pottery photos obtained from online digital libraries. Experiments on identifying the correct pottery shape and pottery schools yielded pproximately 98% accuracy based on the whole pottery image. a