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Abstract：
The exploration of the potential relationship between computable low-level texture, such as features extracted from color and texture, and the perceived high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. First, the selection and clustering of aesthetic antonyms used to represent the aesthetic properties of visual texture are completed through two semantic differential experiments. Subsequently, 151 visual textures are rated according to the selected aesthetic antonyms by participants in a third semantic differential experiment. Third, 106 textural features are extracted using four different image analysis algorithms to describe the low-level characteristics of visual textures. Finally, the construction and evaluation of the visual aesthetic perception model based on multiple linear and nonlinear regression algorithms are discussed. We analyzed the frequency of each aesthetic antonym selected from 20 pairs of semantic antonyms, and the most frequently mentioned 8 pairs of semantic antonyms were selected as the core set for model building. The extracted low-level features are highly correlative. Of the correlation coefficients based on absolute values, 3383 are less than 0.75, accounting for 14.84% of the total. The correlation coefficients were larger than 0.5 accounts for 27.29% of the total. Through neighborhood component analysis, the top 10 low-level features are selected with lower correlation. The gap between low-level calculated features and high-level perceived aesthetic emotions can be bridged by a brain-inspired model of visual aesthetic perception.
摘要：
探索可计算的低级纹理之间的潜在关系，例如从颜色和纹理中提取的特征，以及感知到的高级美学特性，如温暖或寒冷，软或硬，一直是神经美学研究的热点。首先,通过两个语义差异实验完成了用于表示视觉纹理审美属性的审美反义词的选择和聚类。随后，在第三次语义差异实验中，参与者根据选定的审美反义词对151个视觉纹理进行了评级。第三，使用四种不同的图像分析算法提取106个纹理特征，以描述视觉纹理的低级特征。最后,讨论了基于多元线性和非线性回归算法的视觉审美感知模型的构建和评价。我们分析了从20对语义反义词中选择的每个审美反义词的频率，选取最常提及的8对语义反义词作为模型构建的核心集。所提取的低级特征是高度相关的。在基于绝对值的相关系数中，3383低于0.75，占总数的14.84%。相关系数大于0.5的占总数的27.29%。通过邻域成分分析，选择相关性较低的前10个低级特征。低级计算特征和高级感知审美情感之间的差距可以通过大脑启发的视觉审美感知模型来弥合。
