Prediction of Star Polygon Types in Islamic Geometric Patterns with Deep Learning

Abstract

Historical buildings in the Eastern world of architecture host many Islamic geometric patterns which are known as mathematically sophisticated patterns regarding their period of creation. This study focuses on the preparation of a model that can be helpful for the analysis and restoration/maintenance of these patterns. For this, a deep learning model to detect and classify star types in Islamic geometric patterns has been proposed, and the trials were evaluated. Accordingly, this study presents a database containing 5-pointed, 6-pointed, 8-pointed and 12-pointed star types. The database consists of 600 Islamic geometric patterns. A mask RCNN algorithm was trained to detect and classify star types using the prepared database. The results of the training indicate that the loss value is 0.90 and the validation loss value is 0.85. The algorithm was tested using images that it had not seen before and the results were evaluated. This paper presents a discussion on the pros and cons of the trained algorithm.