Application of K-Mean Clustering Algorithm in the Creation of Painting Art

With the continuous development of machine learning in the field of graphics and images, there have been many studies on the implementation methods of real painting simulation. Because there are many places of cross integration between different disciplines, the application scope of machine learning is becoming wider and wider. Therefore, this study uses the k-mean clustering algorithm to segment and extract features of painting works, and then uses the existing graphics and image software to simulate the nature of ink painting and its diffusion, and makes a series of transformation synthesis according to the existing camera photos to simulate and transform the color, line, texture, edge diffusion and other effects of the picture. Finally, the above results are applied to the art creation of traditional painting, which makes the original style of traditional painting more realistic. The experiment shows that using machine learning method to create traditional painting can realize the unity of authenticity and artistically.