A New Tongue Colorchecker Design by Space Representation for Precise Correction

In order to improve the correction accuracy on tongue colors by use of a Munsell colorchecker, this research aims to design a new colorchecker by aid of tongue color space. Three essential issues leading to the development of this space-based colorchecker are elaborately investigated in this study. First, based on a large and comprehensive tongue database, tongue color space is established by which all visible colors can be classified as tongue or nontongue colors. Hence, colors of the designed tongue colorchecker are selected from tongue colors to achieve high correction performance. Second, the minimum sufficient number of colors involved in a colorchecker is yielded by comparing the correction accuracy when different number (ranged from 10 to 200) of colors are contained. Thereby, 24 colors are included because the obtained minimum number of colors is 20. Finally, criteria for optimal color selection and its corresponding objective function are presented. Two color selection methods, i.e., greedy and clustering-based selection method, are proposed to solve the objective function. Experimental results show that clustering-based one outperforms its counterpart to generate the new tongue colorchecker. Compared to a Munsell colorchecker, this proposed space-based colorchecker can greatly improve the correction accuracy by 48%. Further experimental results on more correction task also validate its effectiveness and superiority.