Application of Computer Vision in Automatic Color Extraction and Color Scheme Generation of Oil Paintings
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Abstract
As an important category of visual art, oil painting's use and matching of colors directly determine the emotional expression and aesthetic value of the artwork. In scenarios such as digital preservation, art design assistance, and online exhibitions, how to efficiently and accurately extract representative colors from oil painting images and generate harmonious color schemes has become a research hotspot in the interdisciplinary field of art and technology. This paper constructs a complete methodological system for automatic oil painting color extraction and color scheme generation based on computer vision technology. First, a color feature model of the oil painting image is constructed through color space transformation and image preprocessing. Then, the main color tone is extracted by combining an improved K-Means clustering and color frequency statistics method. Finally, a color scheme generation strategy is constructed based on color harmony theory. Experiments were conducted using a dataset containing 200 oil paintings of different styles, and systematic tests were performed on color extraction accuracy and subjective and objective evaluation indicators of color schemes. The results show that the proposed method achieves an average color extraction accuracy of 91.2%, and the consistency between the color schemes and art expert evaluations is 86.7%, effectively supporting the automated analysis and design application of oil painting colors. This study provides a reproducible technical path for the digital processing of oil painting colors and lays a methodological foundation for the in-depth application of computer vision in the field of art.
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