Traditional Chinese Medicine (TCM) considers that the personal constitution determines the occurrence trend and therapeutic effects of certain diseases, which can be recognized by machine learning through tongue images. However, current machine learning methods are confronted with two challenges. First, there are not some larger tongue image databases available. Second, they do not use the domain knowledge of TCM, so that the imbalance of constitution categories cannot be solved. Therefore, this paper proposes a new constitution recognition method based on the zero-shot learning with the knowledge of TCM. To further improve the performance, a new zero-shot learning method is proposed by grouping attributes and learning discriminant latent features, which can better solve the imbalance problem of constitution categories. Experimental results on our constructed databases validate the proposed methods.

中医认为，个人体质决定了某些疾病的发生趋势和治疗效果，可以通过舌头图像进行机器学习识别。然而，当前的机器学习方法面临两个挑战。首先，没有一些更大的舌头图像数据库可用。其次，他们没有使用中医学的领域知识，因此无法解决体质类别的不平衡问题。因此，本文结合中医知识，提出一种基于零样本学习的体质识别新方法。为了进一步提高性能，提出了一种新的零样本学习方法，通过对属性进行分组和学习判别潜在特征，可以更好地解决构成类别的不平衡问题。