Differential changes in the effective neural drive following new motor skill acquisition between vastus lateralis and medialis.

The art image, as an important carrier of human culture and emotional expression, not only carries rich historical information and aesthetic value but also profoundly reflects the aesthetic concepts and spiritual world of different eras, regions, and individuals. With the rapid development of digital humanities, computer vision, and artificial intelligence technologies, systematic and intelligent analysis and understanding of art images have become a hot topic in interdisciplinary research. Art image processing not only helps in the digital preservation and restoration of cultural heritage but also shows broad application prospects in fields such as art style recognition, authenticity identification, creative content generation, personalized recommendation, and even emotional computing. Especially in the context of the rise of global digital museums, intelligent art platforms, and generative AI art, how to use computational methods to deeply explore the semantic information in art images has become a key bridge connecting technology and humanities. Among the many visual elements of art images, color is recognized as one of the most core expressive dimensions due to its strong perceptual nature, emotional arousal ability, and cultural symbolic significance. Compared to features such as shape, composition, or brushwork, color often more directly affects the subjective experience and emotional response of the viewer—for example, warm tones are often associated with enthusiasm and vitality, while cool tones tend to evoke a sense of tranquility or melancholy. In addition, different art movements (e.g. Impressionism emphasizing light and color changes, Fauvism pursuing high saturation subjective colors) have distinct stylistic characteristics in their use of color. Therefore, accurate modeling and analysis of color not only helps to reveal the inherent stylistic patterns of artworks but also provides important clues for understanding the artist’s creative intentions and the audience’s aesthetic mechanisms. However, traditional color analysis methods (e.g. hand-designed color histograms, color moments, or statistical features in fixed color spaces) often struggle to capture the complex, nonlinear, and highly context-dependent color semantics in art images. In recent years, deep learning technologies, especially Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have demonstrated powerful capabilities in automatic image feature extraction, opening new paths for high-dimensional representation and semantic understanding of color features in art images. Nevertheless, existing research often treats color as part of general visual features, lacking systematic exploration of its unique expressive mechanisms in the artistic context and rarely integrating color psychology, art theory, and computational models for interdisciplinary integration.