Abstract: In order to improve the accuracy and efficiency of traditional art painting style classification, a classification method of traditional art painting style based on color space transformation is proposed. This method preprocesses the traditional artistic painting style, improves the contrast of the image, makes the color and details of the image more vivid, and provides the basis for the subsequent color space conversion. After the traditional artistic painting style is stretched by automatic contrast stretching method, the color space is transformed. The purpose is to transform the image from one color space to another, so as to better extract the features of the image. Based on the traditional artistic painting style image after color space conversion, the traditional artistic painting image is balanced by the adaptive histogram equalization method with limited contrast, and an enhanced traditional artistic painting image is obtained, which further enhances the contrast of the image, makes the details in the image more prominent, and also enhances the overall visual effect of the image. Taking the enhanced traditional art painting images as input, the fuzzy C-means method is used to classify the traditional art painting styles, and the images are effectively divided into different categories according to the characteristics of the images. The experimental results show that this method can effectively enhance the image of traditional art paintings and effectively classify traditional art paintings with different styles, which has strong application effect.

Xu Zhe, “Classification Method of Traditional Art Painting Style Based on Color Space Transformation” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01411102

@article{Zhe2023,
title = {Classification Method of Traditional Art Painting Style Based on Color Space Transformation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411102},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411102},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Xu Zhe}
}