Learning Multi-Granularity Features for Re-Identifying Figures in Portrait Thangka Images

Re-identifying the figures in portrait Thangka images is of great significance to the protection and dissemination of Thangka. Existing portrait Thangka image retrieval methods are limited in extracting features of multiple granularities, and consequently can not distinguish between Thangka figures which have different identities but subtle visual differences. To cope with this challenging task, we propose a Multi-Granularity Feature Learning (MGFL) framework consisting of part and global branches. The part branch splits feature maps along the vertical dimension with inspiration by the composition rules of portrait Thangka images. The global branch integrates inception feature pyramid, channel and spatial attention mechanisms to capture discriminative features of various granularities. To combine the multi-granularity features, we propose to use step-wise graph convolution to better exploit the relationships among different composition elements in portrait Thangka images. Extensive qualitative and quantitative experiments on a dataset with 6,208 portrait Thangka images of 145 identities collected by ourselves demonstrate the superiority of our method. We will release the dataset and code to the public for research purpose.