Preliminary study of tongue image classification based on multi-label learning

Abstract

Tongue diagnosis characterization is a key research issue in the development of Traditional Chinese Medicine (TCM). Many kinds of information, such as tongue body color, coat color and coat thickness, can be reflected from a tongue image. That is, tongue images are multi-label data. However, traditional supervised learning is used to model single-label data. In this paper, multi-label learning is applied to the tongue image classification. Color features and texture features are extracted after separation of tongue coat and body, and multi-label learning algorithms are used for classification. Results showed LEAD (Multi-Label Learning by Exploiting Label Dependency), a multi-label learning algorithm demonstrating to exploit correlations among labels, is superior to the other multi-label algorithms. At last, the iteration algorithm is used to set an optimal threshold for each label to improve the results of LEAD. In this paper, we have provided an effective way for computer aided TCM diagnosis.