Tooth-Marked Tongue Recognition Using Multiple Instance Learning and CNN Features
提出一种三阶段方法，先利用凹陷信息定位疑似齿痕区域，再用卷积神经网络提取深度特征，最后用多实例分类器判断是否为齿痕舌，解决了传统方法在齿痕不凹陷时性能不稳定的问题。
Tooth-marked tongue or crenated tongue can provide valuable diagnostic information for traditional Chinese Medicine doctors. However, tooth-marked tongue recognition is challenging. The characteristics of different tongues are multiform and have a great amount of variations, such as different colors, different shapes, and different types of teeth marks. The regions of teeth mark only appear along the lateral borders. Most existing methods make use of concave regions information to classify the tooth-marked tongue which leads to inconstant performance when the region of teeth mark is not concave. In this paper, we try to solve these problems by proposing a three-stage approach which first makes use of concavity information to propose the suspected regions, then use a convolutional neural network to extract deep features and at last use a multiple-instance classifier to make the final decision. Experimental results demonstrate the effectiveness of the proposed method.