Research on color quality classification algorithms in computer art education

In the realm of art education, the assessment of color blending and color differences has traditionally been a manual process, often subject to human subjectivity and variations in visual acuity. This paper delves into the application of computer vision techniques, a significant branch of artificial intelligence, to address these challenges. By leveraging the high precision, objectivity, and fast processing speed of computer vision, this study aims to revolutionize color inspection in art education. The research encompasses various stages, including art image acquisition, preprocessing, color difference calculation, and feedback provision. A focal point of the study is the extraction of features from art images and the exploration of classification algorithms for online color difference assessment. Through the application of a data modeling method based on the Morlet wavelet kernel SVM, the research achieves an accuracy rate of 88.67% in the classification of fine art images. While promising, the study also identifies areas for further research, including the quantification of color discrepancies in intricate art images and the enhancement of the classification algorithm's operational speed.