Institute of Industrial Design, Zhejiang University of Technology, Hangzhou, China.
College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
PLoS One. 2020 Jan 21;15(1):e0227754. doi: 10.1371/journal.pone.0227754. eCollection 2020.
Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers' ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.
审美感知是人类对多媒体刺激的本能反应。赋予计算机评估人类对美学的感官和感知体验的能力，是智能设计行业和多媒体智能研究的公认需求。在这项工作中，我们构建了一个新的设计审美评估数据库，使用从两个主要设计奖项的档案中收集的 2918 张图像，并提出了一种使用机器学习算法进行审美评估的方法。评审员对设计作品的评分被设定为数据集的真实注释。此外，还提取和融合了多种图像特征。实验结果证明了所提出方法的有效性。使用审美计算进行初步筛选可以成为各种设计评估的智能助手，并可以减少由于长时间观看导致的视觉审美疲劳而对艺术和设计评审产生的误判。计算审美评估的研究可以提高设计评审的效率，并对审美识别探索和应用开发具有重要意义。
