Image-based search and retrieval for biface artefacts using features capturing archaeologically significant characteristics

Abstract

Archaeologists are currently producing huge numbers of digitized photographs to record and preserve artefact finds. These images are used to identify and categorize artefacts and reason about connections between artefacts and perform outreach to the public. However, finding specific types of images within collections remains a major challenge. Often, the metadata associated with images is sparse or is inconsistent. This makes keyword-based exploratory search difficult, leaving researchers to rely on serendipity and slowing down the research process. We present an image-based retrieval system that addresses this problem for biface artefacts. In order to identify artefact characteristics that need to be captured by image features, we conducted a contextual inquiry study with experts in bifaces. We then devised several descriptors for matching images of bifaces with similar artefacts. We evaluated the performance of these descriptors using measures that specifically look at the differences between the sets of images returned by the search system using different descriptors. Through this nuanced approach, we have provided a comprehensive analysis of the strengths and weaknesses of the different descriptors and identified implications for design in the search systems for archaeology.

Keywords: Archaeology; Artifacts; Biface; Flint; Image retrieval; Image-based search.

Fig. 1

Example front, rear, and side views of four bifaces. Each row contains different views of the same artefact

Fig. 2

Preprocessing of images. Annotations and scale are removed, and bounding box of artefact determined

Fig. 3

The radial and angular divisions of the PC map for $P=3$ and $Q=8$. Numbers indicate the sector ordering used when concatenating sector histograms

Fig. 4

Mean and standard deviation of normalized accuracy for each texture descriptor over all queries using Algorithm 1, $N=99$ retrievals