Zhao Yuanyuan
Guangxi Normal University for Nationalities, Chongzuo 532200, China.
Comput Intell Neurosci. 2022 Jun 13;2022:4125833. doi: 10.1155/2022/4125833. eCollection 2022.
Texture has strong expressiveness in picture art, and color texture features play an important role in composition. Together with texture, they can convey the artistic connotation of portrait, especially in oil painting. Therefore, in order to make the picture form oil painting style and oil painting schema, we need to study the texture and color texture in combination with the previous oil painting art images. But now, there are few samples of good oil paintings, so it is difficult to study the texture and color texture in oil paintings. Therefore, in order to form a unique artistic style of modern oil painting and promote the development of modern oil painting art, this paper studies the texture and color texture characteristics in the environment of few oil painting works. This paper establishes a model through deep neural network to extract the image incentive and color texture of oil painting art works, which provides guidance for promoting the development of oil painting art. The experiments in this paper show that the depth neural network has high definition for the extraction of texture and color texture of small sample oil painting images, which can reach more than 85%. It has high guiding significance for the research and creation of oil painting art.
质感在绘画艺术中具有很强的表现力,色彩质感特征在构图中起着重要作用。它们与质感一起,能够传达肖像画的艺术内涵,尤其是在油画中。因此,为了使画面形成油画风格和油画图式,我们需要结合以往的油画艺术图像来研究质感和色彩质感。但目前,优秀的油画样本较少,因此很难对油画中的质感和色彩质感进行研究。所以,为了形成独特的现代油画艺术风格,推动现代油画艺术的发展,本文在油画作品较少的环境下研究质感和色彩质感特征。本文通过深度神经网络建立模型,以提取油画艺术作品的图像激励和色彩质感,为推动油画艺术的发展提供指导。本文的实验表明,深度神经网络对小样本油画图像的质感和色彩质感提取具有较高的清晰度,可达85%以上。这对油画艺术的研究与创作具有很高的指导意义。