Quantified Vector Oriented Tongue Color Classification

Abstract

Tongue diagnosis is a distinctive and essential diagnostic method. The color category of the tongue can be utilized to discover pathological changes on the tongues for identifying diseases. In this paper, a novel scheme is established which classify tongue images into various categories, including coating and substance categories. Firstly, we proposed a two level hierarch clustering method for quantizing all pixels into numerous vectors of feature value. Each vector can code a very small sub-class in RGB color space. Secondly, we utilized the vectors' distribution of these sub-classes to represent approximate chromatic information of tongue images. Then, a Bayesian Network is employed to model the relationship between these quantized vectors and tongue color categories. The effectiveness of this scheme is tested on a group of 418 tongue images, and the classification results are reported.