Chinese Color Name Identification from Images Using a Convolutional Neural Network
- 21 Downloads
Human color vision is a kind of subjective perception. Individual observers may have different color visions. However, the widely used RGB values were highly correlated with the display devices and even the same RGB value represented different colors on various display devices. Furthermore, physically identical patches with identical chromaticity coordinate values had different color perceptions in different spatial contexts. At the same time, individual color vision did not prevent people from color-related communications through color vocabulary. Considering the above problems and the characteristics of color vocabulary used in daily life for communication, we built a color vocabulary labeled data set based on human subjective color visual perception through corresponding visual psychophysics experiments, and then used the data set to train deep neural networks. The network can automatically identify 11 Chinese color vocabularies corresponding to images. The indicator F1-measure of the model reached an accuracy rate of more than 75% in different test sets. Based on the subjective visual perception data set and the deep neural network, we tried to avoid the perceived difference of other color extraction methods.
KeywordsColor recognition Convolutional neural network Multi-Label learning Color vocabulary Deep learning
- 1.Lin, H., Luo, M.R., Macdonald, L.W., Tarrant, A.W.S.: A cross-cultural colour-naming study. Part I: Using an unconstrained method. Color Res. Appl. 26(1), 40–60 (2001)Google Scholar
- 5.De Boer, P.T., Kroese, D.P., Mannor, S., Reuven, Y.: Rubinstein. A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)Google Scholar
- 6.Sasaki, Y.: The truth of the F-measure. Teach Tutor Mater 1(5), 1–5 (2007)Google Scholar