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A method based on the cloud theory was developed to improve the automatic degree and accuracy of chalkiness detection. In this method, without man's intervention, chalkiness and non-chalkiness were defined as two qualitative concepts. An asymmetrical cloud was used to represent chalkiness, and a symmetrical cloud was used to represent non-chalkiness. These two clouds were respectively described by two groups of digital characters. Firstly, dynamic threshold program was designed to acquire training samples for the two clouds. Secondly, backward cloud generators were developed to implement the transformation from the quantities to the qualitatives. Finally, maximum value judgment method was used to separate the chalky region from non-chalky region according to the membership function of each cloud. The result shows that the classification accuracy of cloud classifier is higher than the classification accuracy of traditional hard classifiers. 