| dc.description.abstract | Different uneven weaving methods would cause a large color variability of textiles, increasing the degree of difficulty in detecting colors. To increase the accuracy in color recognition of textiles, we proposed a method combining a self-organizing map (SOM) neural network for color detection in this study. In this method, RGB images were converted into CIE Lab counterparts through color space for colored textiles with complex textures, where the SOM neural network was used to perform color clustering, and to calculate the image color histogram (distribution). Further, main colors of the textiles were extracted from the histogram, and finally the CIE 2000 formula was used to compare the deviation of color measurements, based on which the color recognition was performed. Experimentally, samples of the textile database of ECCV 2016 and filmed textile images with ten colors were employed, where twenty blocks of images were randomly sampled from each textile, for a total of 200 samples used to verify the color recognition method. The results indicated that, for the samples of the textile database of ECCV 2016, using the color detection method proposed in this study could reduce the recognition error rate from 8% (EER=8%) to 3.5% (EER=3.5%), while, for the filmed textile images, using our SOM color detection method could reduce the recognition error rate from 13% (EER=13%) to 1.0% (EER=1.0%) for a number of clusters of 16. | en_US |