Abstract

As a typical application of machine learning in the mobile field of security and privacy protection, the main characteristics of arts and crafts materials are to advocate the practicality and functionality of design, emphasizing the unity of practicality and aesthetics, and practicality is the first. The so-called practicality refers to whether the designed product meets the specific functional requirements or aesthetic requirements, and whether it is convenient, comfortable and safe to use. Based on computer vision, this paper studies the material and aesthetic characteristics of arts and crafts works, and the research shows that this method has achieved the best performance. More specifically, the maximum expected value of this method is 0.69, which is much higher than the corresponding values of 0.54 and 0.50 in references [1] and [2] respectively. The experimental results show that our method is specially designed for the distributed learning of process aesthetics, and it is still a very effective tool for the aesthetic classification task of process aesthetic feature images. Based on computer vision, this paper studies the creative style and aesthetic characteristics of arts and crafts materials, and makes a comprehensive investigation and beneficial exploration on their development, aesthetic characteristics and creative style.

摘要

作为机器学习在安全和隐私保护移动领域的典型应用，工艺美术材料的主要特点是提倡设计的实用性和功能性，强调实用性和审美性的统一，实用性是第一位的。所谓实用性，是指所设计的产品是否满足特定的功能要求或审美要求，使用起来是否方便、舒适、安全。本文基于计算机视觉研究工艺美术作品的材质和审美特征，研究表明该方法取得了最佳性能。更具体地说，该方法的最大期望值为 0.69，远高于参考文献中的对应值 0.54 和 0.50 [1 ] 和 [ 2 ] 分别。实验结果表明，我们的方法是专门为过程美学的分布式学习而设计的，对于过程美学特征图像的美学分类任务仍然是一个非常有效的工具。本文以计算机视觉为基础，研究工艺美术材料的创作风格和审美特征，对其发展历程、审美特征和创作风格进行全面考察和有益探索。