Integration of Multiple Features in Chinese Landscape Painting and Architectural Environment Using Deep Learning Model
Keywords:
Chinese Painting, Landscape, Deep learning, Multiple Features, Architectural Environment, ClassificationAbstract
Chinese painting, renowned for its poetic and artistic representation of landscape and architectural environments, carries a rich cultural legacy. This research paper introduces a novel approach to the integration simulation of Chinese painting landscapes and architectural settings using deep learning techniques, with a focus on multiple feature fusion and optimized classification. The proposed model is stated as the Optimized Multiple Feature Classification (OMFC). The OMFC evaluates the historical significance and artistic value of Chinese painting, which captures the profound beauty of nature and human-made structures in a distinctive style. With the optimization of the painting feature, the architectural environment is evaluated for the estimation of the painting elements. The features estimated are diverse elements such as brushwork styles, color palettes, and artistic motifs combined with architectural environments, blending aesthetics and functionality. The research also introduces optimized classification algorithms that assist in categorizing and harmonizing the different components. These algorithms enhance the accuracy and visual cohesiveness of the integrated simulation, ensuring that the artistic and architectural elements seamlessly coexist. The analysis of the results stated that the proposed OMFC model exhibits significant performance than the conventional techniques. With the implementation of the deep learning model classification and pattern of painting are computed. The convergence of art and architecture through deep learning techniques, opening new horizons for the integration of Chinese painting aesthetics into real-world environments. It advocates for the broader adoption of these methods to promote cultural heritage and innovation in urban and architectural design.
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