Relief Extraction From a Rough Stele Surface Using SVM-Based Relief Segment Selection

Archaeological steles having a rough surface due to long periods of weathering make recognizing the inscription difficult. In this paper, we propose a machine learning-based method to extract reliefs for the inscription from a rough stele surface. Relief candidate segments are initially obtained by using a curvature-based method, which include not only actual reliefs but also noises such as dents and scratches. Then, relief segments are selected using a support vector machine classifier that is trained with various features extracted from relief candidate segments. While conventional methods using a single geometric feature easily fail to detect reliefs from the rough surface, the proposed method utilizes 79-dimensional features consisting of appearance-based, cross section-based, and local extrema-based characteristics of each candidate segment to determine whether the segment is relief or not. Using the proposed method, the inscription of the stele Musul-ojakbi made during the Silla Dynasty AD578 were completely recognized. The experimental results demonstrate that the proposed method accurately extracts reliefs and achieves the highest performance on the rough stele data. The performance of the proposed method is about 8.95% and 10.4% higher than the best of the conventional methods in terms of the F1-score and the SIRI, respectively.

Automated summary

In this paper, a machine learning-based method is proposed to extract reliefs from rough stele surfaces. By utilizing various features to select relief segments from candidate segments, the proposed method successfully recognizes the inscription on the Musul-ojakbi stele made during the Silla Dynasty in AD 578. The experimental results show that the proposed method outperforms conventional methods in terms of F1-score and SIRI, achieving a higher performance in extracting reliefs from rough stele data.