A Semantic Segmentation Model for Headdresses in Thangka Image Based on Line Drawing Augmentation and Spatial Prior Knowledge

Thangka is a religious scroll painting. As a window to Tibetan traditional culture and religious customs, the segmentation of important semantic objects in the images can help the public understand the image content and realize high-level semantic cognition of the visual content. This paper proposes a semantic segmentation network for the central figures’ headdresses in the portrait-type Thangka images based on the rich structure and standard composition of Thangka images. Meanwhile, it has also constructed the first pixel-level semantic annotation data set of the central figures’ headdresses in Thangka images. Firstly, an original Thangka image witnesses edge augmentation through the line drawing augmentation module. Then the Feature Extraction Network is used to extract the feature map. After that, it uses the Half-RPN network to get the region of interest (ROI). Finally, Mask R-CNN Head completes segmentation and class prediction. The experimental results show that the performance of the proposed model is 10% to 19% better than that of the state-of-the-art models such as Mask R-CNN and DeepLab V3.