使用RGB和HSV颜色模型的织物颜色命名的比较

Piyapat Charoensawan, P. Sukanya, I. Shimizu
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引用次数: 5

摘要

本文旨在寻找一种有效的织物颜色识别方法。我们考虑以下颜色命名模型:基本颜色术语,ISCC-NBS颜色系统,Frery的颜色名称和分形实验室的终极颜色词汇。通过使用RGB和HSV颜色模型中的7种最小距离类型为织物图像分配合适的颜色名称。实验在S3纯色织物图像上进行。结果表明,在RGB空间中,利用Frery颜色名称模型和欧几里得距离测量颜色相似度来识别织物颜色名称的方法总体上优于其他方法。通过对织物图像颜色饱和度的进一步分析,我们发现织物图像颜色命名的有效方法如下:Frery的HSV空间二次距离颜色命名模型对于低颜色饱和度组效果较好,准确率为96.23%。Frery在RGB空间中具有欧氏距离的颜色名称模型对于中等颜色饱和度组效果良好(准确率为81.82%)。Fractals Lab的基于HSV空间加权欧几里得距离的Ultimate颜色词汇模型对于高颜色饱和度组效果较好(准确率为76.09%)。
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Comparison of Fabric Color Naming Using RGB and HSV Color Models
This paper aims to find an effective method for identifying colors of fabrics. We consider the following color naming models: Basic Color terms, ISCC-NBS Color system, Frery’s Color name, and Fractals Lab’s Ultimate color vocabulary. The appropriate color name is assigned for a fabric image by using seven types of minimum distance in RGB and HSV color models. Experiments are performed on S3 plain color fabric images. Results show that in general, the method of identifying fabric color name by using Frery’s color name model with the color similarity measure by Euclidean distance in RGB space gives the better result than the other methods. When we analyze in more details by classifying fabric images into three groups based on color saturation, we found that the effective methods for color naming are as follows: Frery’s color name model with the quadratic distance in HSV space works well for the group of low color saturation (96.23% accuracy). Frery’s color name model with Euclidean distance in RGB space works well for the group of medium color saturation (81.82% accuracy). And Fractals Lab’s Ultimate color vocabulary model with the weighted Euclidean distance in HSV space work good for the group of high color saturation (76.09% accuracy).
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