Abstract: Owing to blindness in the selection of tea features in traditional filtering methods and the uncertainty of tea categories, a verification method was proposed to equip each type of tea with a exclusive 0-1 classifier. The positive sample is the target tea itself, the label is 1. The negative sample is the remaining tea type, and the label is 0. During the training process, the model is forced to automatically extract the implicit features that are most suitable for distinguishing the target tea for true and false. This method uses the Siamese network to screen the negative samples, which alleviates the problem of imbalance between positive and negative samples. The experimental results show that this method is well adapted to the disturbance of uncertainty of tea categories and has strong robustness. It is an effective and feasible method.

Key words: binary classifier, tea verification, siamese network, feature extraction