Classification of Dunhuang Mural Image Based on Small-sample and Semi-supervised Learning

Dunhuang mural images classification belongs to the research task in the field of image recognition. In this paper, the semi-supervised model is established with multidimensional features extracted by transfer learning. A small number of labeled samples were used to obtain a large number of unlabeled data, combined with Active Learning and iterative strategy for multiple rounds of label transfer of selected samples. After several rounds of iterations, we can get a more powerful classification learner. Experiments on the self-built Dunhuang mural dataset have shown that the results can approach or even exceed some supervised learning methods when the number of known label samples is less than 4%. Our research implements the cultural resource image classification algorithm based on small samples, which is conductive to improving the accuracy when label samples are scarce.