Pattern recognition by a computer assumes that there is a correct answer in classifying the objects to which we can make reference for correctness of recognition. Classification of a set of objects may have absolutely correct answers when the objects are artifacts (e.g. bolts vs nuts) or highly evolved biological species. However, classification of many other objects is arbitrary (e.g. color, clouds), and is frequently a subject of cultural bias. For instance, traffic lights consist of red, yellow and green in the U.S.A.; they are perceived as red, yellow and blue by Japanese. When human bias is involved in classification, a natural solution is to set a panel of human "experts" and concensus of the panel is assumed to be the correct classification. For instance, expert interior decorators can define classification of different colors and hues, and performance of a machine is tested against the reference set by human experts.