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August 17, 2025Open Access

Enhancing Color Texture Feature Extraction in Comprehensive Material Paintings

Key Points

Algorithm significantly improves color texture feature extraction accuracy and efficiency in art analysis, enhancing traditional methods.
Integration of convolutional neural networks and wavelet transform demonstrates improved feature extraction metrics in comprehensive material paintings.
Research employs rigorous testing to validate the proposed algorithm's performance in bridging technology and artistic techniques.
This work supports the preservation of traditional art forms while embracing modern technological advancements in artistic expression.

Abstract

In the evolving landscape of contemporary art, the integration of diverse materials with artistic expression has led to the emergence of comprehensive material paintings. This innovative approach not only expands traditional boundaries but also introduces personalized schemas and techniques that resonate on aesthetic and emotional levels. Particularly, raw lacquer materials have gained attention for their unique properties, enabling artists to achieve a poetic narrative. This paper proposes an advanced algorithm that integrates Convolutional Neural Networks (CNN) with wavelet transform to enhance color texture feature extraction from comprehensive material paintings. The research tests the hypothesis that this integrated method significantly improves accuracy and efficiency in feature extraction, thereby advancing art analysis and preservation. Through rigorous testing, it is concluded that the proposed algorithm offers valuable insights into bridging traditional art forms with modern technological advancements.

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